Supplementary R script to the RPC report: The effect of Bacillus thuringiensis subsp. israelensis on Chironomus riparius under different exposure conditions. How application time, treatment and larval stage affect toxicity.
Victoria Sophie Stoll
2021-11-01
1 Prepare data
setwd('C:/Users/Sophie/Desktop/M_Sc_Ecotoxicology/Research_Project_Course/Data/R_Analysis')
data = read.csv('C:/Users/Sophie/Desktop/M_Sc_Ecotoxicology/Research_Project_Course/Data/R_Analysis/Sensitivity_data_kumulativ_DAUER_CORR.csv') # load data
# load packages ----
require(data.table)
require(ggplot2)
require(dplyr)
require(gridExtra)
require(ggpubr)
require(tidyverse)
library(multcomp)
library(knitr)
library(rstatix)
library(ggpubr)
library(grid)
library(pBrackets)
library(broom)
data <- setDT(data) # set to data.table
data[ , Date := as.Date(Date, format = "%d/%m/%Y")]
data[ , Day := yday(Date) - 61 ] # extract day after start of emergence
data[ , Percent_Ind := Ind/20] # percent individuals of 20 individuals in total
data[ , Percent_Ind_M := M/20] # male
data[ , Percent_Ind_F := F/20] # female
data$Treatment[data$Treatment == '5%'] <- '05%'
data[ , Rpl := as.character(Rpl)]
data[ , Bti_application := as.factor(Bti_application)]
data[ , Treatment := as.factor(Treatment)]
data[ , Larval_stage := as.factor(Larval_stage)]
data[ , Date := as.character(Date)]
data[is.na(data)] <- 0 # convert NA to 0
levels(data$Treatment) # check if levels are in the right order
data <- data %>%
reorder_levels(Treatment, order = c("Ctrl", "05%", "10%", "25%", "50%")) # reorder
levels(data$Bti_application) # check if levels are in the right order
data <- data %>%
reorder_levels(Bti_application, order = c("none", "d0", "d05", "d10", "d15")) # reorder
levels(data$Larval_stage) # check if levels are in the right order
data <- data %>%
reorder_levels(Larval_stage, order = c("L1", "Lx")) # reorder
application <- c("none" = "green", "d0" = "dark blue", "d05" = "blue", "d10" = "light blue", "d15" = "grey" )
treatment <- c("Ctrl" = "green", "05%" = "yellow", "10%" = "orange", "25%" = "red", "50%" = "dark red")2 Overview
Description of the (relevant) variables in the data set.
| Variable | Description |
|---|---|
| Treatment | Bti concentration (5, 10, 25, 50% of field rate, Ctrl) |
| Bti_application/Time | Day of application before test start (=d0) (d0, d05, d10, d15) |
| Larval_stage | Larval_stage at test start (L1, Lx (later)) |
| Day | Day afer start of emergence |
| Ind/All | Number of individuals emerged |
| M | Number of males emerged in one day per test unit |
| F | Number of females emerged |
| Emergence_all | Duration from start of emergence till last day of emergence (per test unit) |
| Emergence_M | Duration from start of emergence till last day of emergence (per test unit) (only males) |
| Emergence_F | Duration from start of emergence till last day of emergence (per test unit) (only females) |
| Mean_LD_Ind | Mean larval development (Duration from hatching till emergence) |
| Mean_LD_M | Mean larval development (Duration from hatching till emergence) (only males) |
| Mean_LD_F | Mean larval development (Duration from hatching till emergence) (only females) |
| Percentage_M | Percentage of males emerged |
- The investigated endpoints are:
- Number of individuals emerged (all, M, F)
- Duration of emergence (all, M, F)
- Mean duration of larval development (all, M, F)
- Percentage of emerged males (gender ratio, only M)
- The independent variables are:
- Treatment (Bti amount)
- Bti application (day of application)
- Larval stage
- Hypotheses:
- Bti effect mitigates with increasing application time but is still visible at d15
- The effect is dependent on the treatment (increasing effect with increasing treatment)
- Earlier larval stages are more sensitive than later larval stages
- There are different effects in males and females
3 ANOVAs
3.1 No. of individuals
# Treatment
aov_Treatment <- aov(Percent_Ind ~ Treatment, data = data[Day == 17])
summary(aov_Treatment)## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 4 1.079 0.26965 3.391 0.0122 *
## Residuals 97 7.714 0.07953
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ####### Check model assumptions:
plot(aov_Treatment) 
#Residuals vs. fitted values: approx. around 0, horizontal line. Die Säulen-Anordnung ist normal für ANOVA
#Normal QQ values around dashed line, no other patterns.
#even distribution around approx. horizontal line. Between 2.5 and -2.5.
#All data points inside cooks distance (lines) (outliers are no influential points).
TukeyHSD(aov_Treatment)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Percent_Ind ~ Treatment, data = data[Day == 17])
##
## $Treatment
## diff lwr upr p adj
## 05%-Ctrl 0.06041667 -0.2973821 0.41821547 0.9899051
## 10%-Ctrl 0.04375000 -0.3140488 0.40154881 0.9970814
## 25%-Ctrl -0.06250000 -0.4202988 0.29529881 0.9885238
## 50%-Ctrl -0.20416667 -0.5619655 0.15363214 0.5098082
## 10%-05% -0.01666667 -0.2429585 0.20962517 0.9996019
## 25%-05% -0.12291667 -0.3492085 0.10337517 0.5586298
## 50%-05% -0.26458333 -0.4908752 -0.03829150 0.0134392
## 25%-10% -0.10625000 -0.3325418 0.12004183 0.6886624
## 50%-10% -0.24791667 -0.4742085 -0.02162483 0.0243853
## 50%-25% -0.14166667 -0.3679585 0.08462517 0.4145362aov_Treatment_M <- aov(Percent_Ind_M ~ Treatment, data = data[Day == 17])
summary(aov_Treatment_M)## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 4 0.3359 0.08399 3.095 0.0192 *
## Residuals 97 2.6323 0.02714
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Treatment_M) 
TukeyHSD(aov_Treatment_M)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Percent_Ind_M ~ Treatment, data = data[Day == 17])
##
## $Treatment
## diff lwr upr p adj
## 05%-Ctrl -3.330669e-16 -0.20901009 0.209010087 1.0000000
## 10%-Ctrl 6.875000e-02 -0.14026009 0.277760087 0.8907478
## 25%-Ctrl -6.041667e-02 -0.26942675 0.148593421 0.9289457
## 50%-Ctrl -8.333333e-02 -0.29234342 0.125676754 0.8018009
## 10%-05% 6.875000e-02 -0.06343959 0.200939586 0.5999039
## 25%-05% -6.041667e-02 -0.19260625 0.071772919 0.7098189
## 50%-05% -8.333333e-02 -0.21552292 0.048856253 0.4073143
## 25%-10% -1.291667e-01 -0.26135625 0.003022919 0.0587988
## 50%-10% -1.520833e-01 -0.28427292 -0.019893747 0.0156888
## 50%-25% -2.291667e-02 -0.15510625 0.109272919 0.9888473aov_Treatment_F <- aov(Percent_Ind_F ~ Treatment, data = data[Day == 17])
summary(aov_Treatment_F)## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 4 0.410 0.10247 2.968 0.0232 *
## Residuals 97 3.349 0.03452
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Treatment_F) 
TukeyHSD(aov_Treatment_F)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Percent_Ind_F ~ Treatment, data = data[Day == 17])
##
## $Treatment
## diff lwr upr p adj
## 05%-Ctrl 0.060416667 -0.1753205 0.29615385 0.9532004
## 10%-Ctrl -0.025000000 -0.2607372 0.21073719 0.9983256
## 25%-Ctrl -0.002083333 -0.2378205 0.23365385 0.9999999
## 50%-Ctrl -0.120833333 -0.3565705 0.11490385 0.6132877
## 10%-05% -0.085416667 -0.2345100 0.06367662 0.5057670
## 25%-05% -0.062500000 -0.2115933 0.08659329 0.7709995
## 50%-05% -0.181250000 -0.3303433 -0.03215671 0.0090650
## 25%-10% 0.022916667 -0.1261766 0.17200995 0.9929428
## 50%-10% -0.095833333 -0.2449266 0.05325995 0.3871817
## 50%-25% -0.118750000 -0.2678433 0.03034329 0.1833040#Time (Bti_application)
aov_Bti_application <- aov(Percent_Ind ~ Bti_application, data = data[Day == 17])
summary(aov_Bti_application)## Df Sum Sq Mean Sq F value Pr(>F)
## Bti_application 4 3.025 0.7564 12.72 2.31e-08 ***
## Residuals 97 5.767 0.0595
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Bti_application)
TukeyHSD(aov_Bti_application)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Percent_Ind ~ Bti_application, data = data[Day == 17])
##
## $Bti_application
## diff lwr upr p adj
## d0-none -0.33333333 -0.64270345 -0.02396322 0.0280758
## d05-none -0.01250000 -0.32187011 0.29687011 0.9999634
## d10-none 0.04583333 -0.26353678 0.35520345 0.9938714
## d15-none 0.13750000 -0.17187011 0.44687011 0.7307206
## d05-d0 0.32083333 0.12517049 0.51649617 0.0001445
## d10-d0 0.37916667 0.18350383 0.57482951 0.0000049
## d15-d0 0.47083333 0.27517049 0.66649617 0.0000000
## d10-d05 0.05833333 -0.13732951 0.25399617 0.9210998
## d15-d05 0.15000000 -0.04566284 0.34566284 0.2155056
## d15-d10 0.09166667 -0.10399617 0.28732951 0.6904293aov_Bti_application_M <- aov(Percent_Ind_M ~ Bti_application, data = data[Day == 17])
summary(aov_Bti_application_M)## Df Sum Sq Mean Sq F value Pr(>F)
## Bti_application 4 0.7997 0.19992 8.943 3.44e-06 ***
## Residuals 97 2.1685 0.02236
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Bti_application_M)
TukeyHSD(aov_Bti_application_M)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Percent_Ind_M ~ Bti_application, data = data[Day == 17])
##
## $Bti_application
## diff lwr upr p adj
## d0-none -0.168750000 -0.35845734 0.02095734 0.1054533
## d05-none -0.004166667 -0.19387400 0.18554067 0.9999968
## d10-none 0.022916667 -0.16679067 0.21262400 0.9972160
## d15-none 0.075000000 -0.11470734 0.26470734 0.8066676
## d05-d0 0.164583333 0.04460188 0.28456479 0.0022008
## d10-d0 0.191666667 0.07168521 0.31164812 0.0002270
## d15-d0 0.243750000 0.12376855 0.36373145 0.0000016
## d10-d05 0.027083333 -0.09289812 0.14706479 0.9702996
## d15-d05 0.079166667 -0.04081479 0.19914812 0.3601917
## d15-d10 0.052083333 -0.06789812 0.17206479 0.7475236aov_Bti_application_F <- aov(Percent_Ind_F ~ Bti_application, data = data[Day == 17])
summary(aov_Bti_application_F)## Df Sum Sq Mean Sq F value Pr(>F)
## Bti_application 4 0.7149 0.17872 5.696 0.000368 ***
## Residuals 97 3.0435 0.03138
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Bti_application_F)
#Larval_stage
aov_Larval_stage <- aov(Percent_Ind ~ Larval_stage, data = data[Day == 17])
summary(aov_Larval_stage)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 0.365 0.3648 4.329 0.04 *
## Residuals 100 8.428 0.0843
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage)
TukeyHSD(aov_Larval_stage)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Percent_Ind ~ Larval_stage, data = data[Day == 17])
##
## $Larval_stage
## diff lwr upr p adj
## Lx-L1 0.1196078 0.005551081 0.2336646 0.0400331aov_Larval_stage_M <- aov(Percent_Ind_M ~ Larval_stage, data = data[Day == 17])
summary(aov_Larval_stage_M)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 0.0192 0.01922 0.652 0.421
## Residuals 100 2.9490 0.02949plot(aov_Larval_stage_M) 
TukeyHSD(aov_Larval_stage_M)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Percent_Ind_M ~ Larval_stage, data = data[Day == 17])
##
## $Larval_stage
## diff lwr upr p adj
## Lx-L1 0.02745098 -0.040018 0.09491996 0.4214586aov_Larval_stage_F <- aov(Percent_Ind_F ~ Larval_stage, data = data[Day == 17])
summary(aov_Larval_stage_F)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 0.217 0.21657 6.115 0.0151 *
## Residuals 100 3.542 0.03542
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_F)
TukeyHSD(aov_Larval_stage_F)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Percent_Ind_F ~ Larval_stage, data = data[Day == 17])
##
## $Larval_stage
## diff lwr upr p adj
## Lx-L1 0.09215686 0.01821656 0.1660972 0.0150942#Treatment x Bti_application
aov_Treatment_appl <- aov(Percent_Ind ~ Treatment * Bti_application, data = data[Day == 17])
summary(aov_Treatment_appl)## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 4 1.079 0.2696 5.646 0.000444 ***
## Bti_application 3 3.016 1.0054 21.053 2.73e-10 ***
## Treatment:Bti_application 9 0.639 0.0710 1.486 0.166258
## Residuals 85 4.059 0.0478
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Treatment_appl) 
TukeyHSD(aov_Treatment_appl)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Percent_Ind ~ Treatment * Bti_application, data = data[Day == 17])
##
## $Treatment
## diff lwr upr p adj
## 05%-Ctrl 0.06041667 -0.2175907 0.33842407 0.9738320
## 10%-Ctrl 0.04375000 -0.2342574 0.32175740 0.9921807
## 25%-Ctrl -0.06250000 -0.3405074 0.21550740 0.9703936
## 50%-Ctrl -0.20416667 -0.4821741 0.07384074 0.2530406
## 10%-05% -0.01666667 -0.1924940 0.15916065 0.9989080
## 25%-05% -0.12291667 -0.2987440 0.05291065 0.3002582
## 50%-05% -0.26458333 -0.4404107 -0.08875601 0.0006268
## 25%-10% -0.10625000 -0.2820773 0.06957732 0.4490705
## 50%-10% -0.24791667 -0.4237440 -0.07208935 0.0015832
## 50%-25% -0.14166667 -0.3174940 0.03416065 0.1732203
##
## $Bti_application
## diff lwr upr p adj
## d0-none -0.29270833 -0.57071574 -0.01470093 0.0339609
## d05-none 0.02812500 -0.24988240 0.30613240 0.9985898
## d10-none 0.08645833 -0.19154907 0.36446574 0.9082088
## d15-none 0.17812500 -0.09988240 0.45613240 0.3885417
## d05-d0 0.32083333 0.14500601 0.49666065 0.0000210
## d10-d0 0.37916667 0.20333935 0.55499399 0.0000004
## d15-d0 0.47083333 0.29500601 0.64666065 0.0000000
## d10-d05 0.05833333 -0.11749399 0.23416065 0.8865889
## d15-d05 0.15000000 -0.02582732 0.32582732 0.1314844
## d15-d10 0.09166667 -0.08416065 0.26749399 0.5954487
##
## $`Treatment:Bti_application`
## diff lwr upr p adj
## 05%:none-Ctrl:none NA NA NA NA
## 10%:none-Ctrl:none NA NA NA NA
## 25%:none-Ctrl:none NA NA NA NA
## 50%:none-Ctrl:none NA NA NA NA
## Ctrl:d0-Ctrl:none NA NA NA NA
## 05%:d0-Ctrl:none -7.500000e-02 -0.55215139 0.40215139 1.0000000
## 10%:d0-Ctrl:none -2.500000e-01 -0.72715139 0.22715139 0.9498490
## 25%:d0-Ctrl:none -3.916667e-01 -0.86881806 0.08548472 0.2719771
## 50%:d0-Ctrl:none -6.166667e-01 -1.09381806 -0.13951528 0.0011799
## Ctrl:d05-Ctrl:none NA NA NA NA
## 05%:d05-Ctrl:none 5.000000e-02 -0.42715139 0.52715139 1.0000000
## 10%:d05-Ctrl:none 1.000000e-01 -0.37715139 0.57715139 1.0000000
## 25%:d05-Ctrl:none 5.000000e-02 -0.42715139 0.52715139 1.0000000
## 50%:d05-Ctrl:none -2.500000e-01 -0.72715139 0.22715139 0.9498490
## Ctrl:d10-Ctrl:none NA NA NA NA
## 05%:d10-Ctrl:none 1.166667e-01 -0.36048472 0.59381806 0.9999996
## 10%:d10-Ctrl:none 1.166667e-01 -0.36048472 0.59381806 0.9999996
## 25%:d10-Ctrl:none 5.833333e-02 -0.41881806 0.53548472 1.0000000
## 50%:d10-Ctrl:none -1.083333e-01 -0.58548472 0.36881806 0.9999999
## Ctrl:d15-Ctrl:none NA NA NA NA
## 05%:d15-Ctrl:none 1.500000e-01 -0.32715139 0.62715139 0.9999601
## 10%:d15-Ctrl:none 2.083333e-01 -0.26881806 0.68548472 0.9938235
## 25%:d15-Ctrl:none 3.333333e-02 -0.44381806 0.51048472 1.0000000
## 50%:d15-Ctrl:none 1.583333e-01 -0.31881806 0.63548472 0.9998993
## 10%:none-05%:none NA NA NA NA
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## 05%:d15-Ctrl:d0 NA NA NA NA
## 10%:d15-Ctrl:d0 NA NA NA NA
## 25%:d15-Ctrl:d0 NA NA NA NA
## 50%:d15-Ctrl:d0 NA NA NA NA
## 10%:d0-05%:d0 -1.750000e-01 -0.65215139 0.30215139 0.9994906
## 25%:d0-05%:d0 -3.166667e-01 -0.79381806 0.16048472 0.6841497
## 50%:d0-05%:d0 -5.416667e-01 -1.01881806 -0.06451528 0.0099670
## Ctrl:d05-05%:d0 NA NA NA NA
## 05%:d05-05%:d0 1.250000e-01 -0.35215139 0.60215139 0.9999986
## 10%:d05-05%:d0 1.750000e-01 -0.30215139 0.65215139 0.9994906
## 25%:d05-05%:d0 1.250000e-01 -0.35215139 0.60215139 0.9999986
## 50%:d05-05%:d0 -1.750000e-01 -0.65215139 0.30215139 0.9994906
## Ctrl:d10-05%:d0 NA NA NA NA
## 05%:d10-05%:d0 1.916667e-01 -0.28548472 0.66881806 0.9980202
## 10%:d10-05%:d0 1.916667e-01 -0.28548472 0.66881806 0.9980202
## 25%:d10-05%:d0 1.333333e-01 -0.34381806 0.61048472 0.9999952
## 50%:d10-05%:d0 -3.333333e-02 -0.51048472 0.44381806 1.0000000
## Ctrl:d15-05%:d0 NA NA NA NA
## 05%:d15-05%:d0 2.250000e-01 -0.25215139 0.70215139 0.9839892
## 10%:d15-05%:d0 2.833333e-01 -0.19381806 0.76048472 0.8493102
## 25%:d15-05%:d0 1.083333e-01 -0.36881806 0.58548472 0.9999999
## 50%:d15-05%:d0 2.333333e-01 -0.24381806 0.71048472 0.9757343
## 25%:d0-10%:d0 -1.416667e-01 -0.61881806 0.33548472 0.9999855
## 50%:d0-10%:d0 -3.666667e-01 -0.84381806 0.11048472 0.3950190
## Ctrl:d05-10%:d0 NA NA NA NA
## 05%:d05-10%:d0 3.000000e-01 -0.17715139 0.77715139 0.7733842
## 10%:d05-10%:d0 3.500000e-01 -0.12715139 0.82715139 0.4888617
## 25%:d05-10%:d0 3.000000e-01 -0.17715139 0.77715139 0.7733842
## 50%:d05-10%:d0 0.000000e+00 -0.47715139 0.47715139 1.0000000
## Ctrl:d10-10%:d0 NA NA NA NA
## 05%:d10-10%:d0 3.666667e-01 -0.11048472 0.84381806 0.3950190
## 10%:d10-10%:d0 3.666667e-01 -0.11048472 0.84381806 0.3950190
## 25%:d10-10%:d0 3.083333e-01 -0.16881806 0.78548472 0.7301154
## 50%:d10-10%:d0 1.416667e-01 -0.33548472 0.61881806 0.9999855
## Ctrl:d15-10%:d0 NA NA NA NA
## 05%:d15-10%:d0 4.000000e-01 -0.07715139 0.87715139 0.2369328
## 10%:d15-10%:d0 4.583333e-01 -0.01881806 0.93548472 0.0762205
## 25%:d15-10%:d0 2.833333e-01 -0.19381806 0.76048472 0.8493102
## 50%:d15-10%:d0 4.083333e-01 -0.06881806 0.88548472 0.2050803
## 50%:d0-25%:d0 -2.250000e-01 -0.70215139 0.25215139 0.9839892
## Ctrl:d05-25%:d0 NA NA NA NA
## 05%:d05-25%:d0 4.416667e-01 -0.03548472 0.91881806 0.1083883
## 10%:d05-25%:d0 4.916667e-01 0.01451528 0.96881806 0.0355391
## 25%:d05-25%:d0 4.416667e-01 -0.03548472 0.91881806 0.1083883
## 50%:d05-25%:d0 1.416667e-01 -0.33548472 0.61881806 0.9999855
## Ctrl:d10-25%:d0 NA NA NA NA
## 05%:d10-25%:d0 5.083333e-01 0.03118194 0.98548472 0.0236301
## 10%:d10-25%:d0 5.083333e-01 0.03118194 0.98548472 0.0236301
## 25%:d10-25%:d0 4.500000e-01 -0.02715139 0.92715139 0.0911294
## 50%:d10-25%:d0 2.833333e-01 -0.19381806 0.76048472 0.8493102
## Ctrl:d15-25%:d0 NA NA NA NA
## 05%:d15-25%:d0 5.416667e-01 0.06451528 1.01881806 0.0099670
## 10%:d15-25%:d0 6.000000e-01 0.12284861 1.07715139 0.0019341
## 25%:d15-25%:d0 4.250000e-01 -0.05215139 0.90215139 0.1508345
## 50%:d15-25%:d0 5.500000e-01 0.07284861 1.02715139 0.0079605
## Ctrl:d05-50%:d0 NA NA NA NA
## 05%:d05-50%:d0 6.666667e-01 0.18951528 1.14381806 0.0002529
## 10%:d05-50%:d0 7.166667e-01 0.23951528 1.19381806 0.0000504
## 25%:d05-50%:d0 6.666667e-01 0.18951528 1.14381806 0.0002529
## 50%:d05-50%:d0 3.666667e-01 -0.11048472 0.84381806 0.3950190
## Ctrl:d10-50%:d0 NA NA NA NA
## 05%:d10-50%:d0 7.333333e-01 0.25618194 1.21048472 0.0000290
## 10%:d10-50%:d0 7.333333e-01 0.25618194 1.21048472 0.0000290
## 25%:d10-50%:d0 6.750000e-01 0.19784861 1.15215139 0.0001942
## 50%:d10-50%:d0 5.083333e-01 0.03118194 0.98548472 0.0236301
## Ctrl:d15-50%:d0 NA NA NA NA
## 05%:d15-50%:d0 7.666667e-01 0.28951528 1.24381806 0.0000095
## 10%:d15-50%:d0 8.250000e-01 0.34784861 1.30215139 0.0000013
## 25%:d15-50%:d0 6.500000e-01 0.17284861 1.12715139 0.0004264
## 50%:d15-50%:d0 7.750000e-01 0.29784861 1.25215139 0.0000071
## 05%:d05-Ctrl:d05 NA NA NA NA
## 10%:d05-Ctrl:d05 NA NA NA NA
## 25%:d05-Ctrl:d05 NA NA NA NA
## 50%:d05-Ctrl:d05 NA NA NA NA
## Ctrl:d10-Ctrl:d05 NA NA NA NA
## 05%:d10-Ctrl:d05 NA NA NA NA
## 10%:d10-Ctrl:d05 NA NA NA NA
## 25%:d10-Ctrl:d05 NA NA NA NA
## 50%:d10-Ctrl:d05 NA NA NA NA
## Ctrl:d15-Ctrl:d05 NA NA NA NA
## 05%:d15-Ctrl:d05 NA NA NA NA
## 10%:d15-Ctrl:d05 NA NA NA NA
## 25%:d15-Ctrl:d05 NA NA NA NA
## 50%:d15-Ctrl:d05 NA NA NA NA
## 10%:d05-05%:d05 5.000000e-02 -0.42715139 0.52715139 1.0000000
## 25%:d05-05%:d05 3.330669e-16 -0.47715139 0.47715139 1.0000000
## 50%:d05-05%:d05 -3.000000e-01 -0.77715139 0.17715139 0.7733842
## Ctrl:d10-05%:d05 NA NA NA NA
## 05%:d10-05%:d05 6.666667e-02 -0.41048472 0.54381806 1.0000000
## 10%:d10-05%:d05 6.666667e-02 -0.41048472 0.54381806 1.0000000
## 25%:d10-05%:d05 8.333333e-03 -0.46881806 0.48548472 1.0000000
## 50%:d10-05%:d05 -1.583333e-01 -0.63548472 0.31881806 0.9998993
## Ctrl:d15-05%:d05 NA NA NA NA
## 05%:d15-05%:d05 1.000000e-01 -0.37715139 0.57715139 1.0000000
## 10%:d15-05%:d05 1.583333e-01 -0.31881806 0.63548472 0.9998993
## 25%:d15-05%:d05 -1.666667e-02 -0.49381806 0.46048472 1.0000000
## 50%:d15-05%:d05 1.083333e-01 -0.36881806 0.58548472 0.9999999
## 25%:d05-10%:d05 -5.000000e-02 -0.52715139 0.42715139 1.0000000
## 50%:d05-10%:d05 -3.500000e-01 -0.82715139 0.12715139 0.4888617
## Ctrl:d10-10%:d05 NA NA NA NA
## 05%:d10-10%:d05 1.666667e-02 -0.46048472 0.49381806 1.0000000
## 10%:d10-10%:d05 1.666667e-02 -0.46048472 0.49381806 1.0000000
## 25%:d10-10%:d05 -4.166667e-02 -0.51881806 0.43548472 1.0000000
## 50%:d10-10%:d05 -2.083333e-01 -0.68548472 0.26881806 0.9938235
## Ctrl:d15-10%:d05 NA NA NA NA
## 05%:d15-10%:d05 5.000000e-02 -0.42715139 0.52715139 1.0000000
## 10%:d15-10%:d05 1.083333e-01 -0.36881806 0.58548472 0.9999999
## 25%:d15-10%:d05 -6.666667e-02 -0.54381806 0.41048472 1.0000000
## 50%:d15-10%:d05 5.833333e-02 -0.41881806 0.53548472 1.0000000
## 50%:d05-25%:d05 -3.000000e-01 -0.77715139 0.17715139 0.7733842
## Ctrl:d10-25%:d05 NA NA NA NA
## 05%:d10-25%:d05 6.666667e-02 -0.41048472 0.54381806 1.0000000
## 10%:d10-25%:d05 6.666667e-02 -0.41048472 0.54381806 1.0000000
## 25%:d10-25%:d05 8.333333e-03 -0.46881806 0.48548472 1.0000000
## 50%:d10-25%:d05 -1.583333e-01 -0.63548472 0.31881806 0.9998993
## Ctrl:d15-25%:d05 NA NA NA NA
## 05%:d15-25%:d05 1.000000e-01 -0.37715139 0.57715139 1.0000000
## 10%:d15-25%:d05 1.583333e-01 -0.31881806 0.63548472 0.9998993
## 25%:d15-25%:d05 -1.666667e-02 -0.49381806 0.46048472 1.0000000
## 50%:d15-25%:d05 1.083333e-01 -0.36881806 0.58548472 0.9999999
## Ctrl:d10-50%:d05 NA NA NA NA
## 05%:d10-50%:d05 3.666667e-01 -0.11048472 0.84381806 0.3950190
## 10%:d10-50%:d05 3.666667e-01 -0.11048472 0.84381806 0.3950190
## 25%:d10-50%:d05 3.083333e-01 -0.16881806 0.78548472 0.7301154
## 50%:d10-50%:d05 1.416667e-01 -0.33548472 0.61881806 0.9999855
## Ctrl:d15-50%:d05 NA NA NA NA
## 05%:d15-50%:d05 4.000000e-01 -0.07715139 0.87715139 0.2369328
## 10%:d15-50%:d05 4.583333e-01 -0.01881806 0.93548472 0.0762205
## 25%:d15-50%:d05 2.833333e-01 -0.19381806 0.76048472 0.8493102
## 50%:d15-50%:d05 4.083333e-01 -0.06881806 0.88548472 0.2050803
## 05%:d10-Ctrl:d10 NA NA NA NA
## 10%:d10-Ctrl:d10 NA NA NA NA
## 25%:d10-Ctrl:d10 NA NA NA NA
## 50%:d10-Ctrl:d10 NA NA NA NA
## Ctrl:d15-Ctrl:d10 NA NA NA NA
## 05%:d15-Ctrl:d10 NA NA NA NA
## 10%:d15-Ctrl:d10 NA NA NA NA
## 25%:d15-Ctrl:d10 NA NA NA NA
## 50%:d15-Ctrl:d10 NA NA NA NA
## 10%:d10-05%:d10 4.440892e-16 -0.47715139 0.47715139 1.0000000
## 25%:d10-05%:d10 -5.833333e-02 -0.53548472 0.41881806 1.0000000
## 50%:d10-05%:d10 -2.250000e-01 -0.70215139 0.25215139 0.9839892
## Ctrl:d15-05%:d10 NA NA NA NA
## 05%:d15-05%:d10 3.333333e-02 -0.44381806 0.51048472 1.0000000
## 10%:d15-05%:d10 9.166667e-02 -0.38548472 0.56881806 1.0000000
## 25%:d15-05%:d10 -8.333333e-02 -0.56048472 0.39381806 1.0000000
## 50%:d15-05%:d10 4.166667e-02 -0.43548472 0.51881806 1.0000000
## 25%:d10-10%:d10 -5.833333e-02 -0.53548472 0.41881806 1.0000000
## 50%:d10-10%:d10 -2.250000e-01 -0.70215139 0.25215139 0.9839892
## Ctrl:d15-10%:d10 NA NA NA NA
## 05%:d15-10%:d10 3.333333e-02 -0.44381806 0.51048472 1.0000000
## 10%:d15-10%:d10 9.166667e-02 -0.38548472 0.56881806 1.0000000
## 25%:d15-10%:d10 -8.333333e-02 -0.56048472 0.39381806 1.0000000
## 50%:d15-10%:d10 4.166667e-02 -0.43548472 0.51881806 1.0000000
## 50%:d10-25%:d10 -1.666667e-01 -0.64381806 0.31048472 0.9997652
## Ctrl:d15-25%:d10 NA NA NA NA
## 05%:d15-25%:d10 9.166667e-02 -0.38548472 0.56881806 1.0000000
## 10%:d15-25%:d10 1.500000e-01 -0.32715139 0.62715139 0.9999601
## 25%:d15-25%:d10 -2.500000e-02 -0.50215139 0.45215139 1.0000000
## 50%:d15-25%:d10 1.000000e-01 -0.37715139 0.57715139 1.0000000
## Ctrl:d15-50%:d10 NA NA NA NA
## 05%:d15-50%:d10 2.583333e-01 -0.21881806 0.73548472 0.9312435
## 10%:d15-50%:d10 3.166667e-01 -0.16048472 0.79381806 0.6841497
## 25%:d15-50%:d10 1.416667e-01 -0.33548472 0.61881806 0.9999855
## 50%:d15-50%:d10 2.666667e-01 -0.21048472 0.74381806 0.9083766
## 05%:d15-Ctrl:d15 NA NA NA NA
## 10%:d15-Ctrl:d15 NA NA NA NA
## 25%:d15-Ctrl:d15 NA NA NA NA
## 50%:d15-Ctrl:d15 NA NA NA NA
## 10%:d15-05%:d15 5.833333e-02 -0.41881806 0.53548472 1.0000000
## 25%:d15-05%:d15 -1.166667e-01 -0.59381806 0.36048472 0.9999996
## 50%:d15-05%:d15 8.333333e-03 -0.46881806 0.48548472 1.0000000
## 25%:d15-10%:d15 -1.750000e-01 -0.65215139 0.30215139 0.9994906
## 50%:d15-10%:d15 -5.000000e-02 -0.52715139 0.42715139 1.0000000
## 50%:d15-25%:d15 1.250000e-01 -0.35215139 0.60215139 0.9999986aov_Treatment_appl_M <- aov(M ~ Treatment * Bti_application, data = data[Day == 17])
summary(aov_Treatment_appl_M)## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 4 134.4 33.59 4.992 0.00116 **
## Bti_application 3 319.1 106.36 15.805 2.98e-08 ***
## Treatment:Bti_application 9 161.8 17.98 2.672 0.00881 **
## Residuals 85 572.0 6.73
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Treatment_appl_M)
shapiro_test(residuals(aov_Treatment_appl_M)) ## # A tibble: 1 x 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 residuals(aov_Treatment_appl_M) 0.989 0.571TukeyHSD(aov_Treatment_appl_M)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = M ~ Treatment * Bti_application, data = data[Day == 17])
##
## $Treatment
## diff lwr upr p adj
## 05%-Ctrl -7.105427e-15 -3.300167 3.3001671 1.0000000
## 10%-Ctrl 1.375000e+00 -1.925167 4.6751671 0.7731883
## 25%-Ctrl -1.208333e+00 -4.508500 2.0918338 0.8452439
## 50%-Ctrl -1.666667e+00 -4.966834 1.6335005 0.6245210
## 10%-05% 1.375000e+00 -0.712209 3.4622090 0.3599719
## 25%-05% -1.208333e+00 -3.295542 0.8788756 0.4929948
## 50%-05% -1.666667e+00 -3.753876 0.4205423 0.1803334
## 25%-10% -2.583333e+00 -4.670542 -0.4961244 0.0076137
## 50%-10% -3.041667e+00 -5.128876 -0.9544577 0.0010024
## 50%-25% -4.583333e-01 -2.545542 1.6288756 0.9728200
##
## $Bti_application
## diff lwr upr p adj
## d0-none -3.0000000 -6.3001671 0.3001671 0.0927747
## d05-none 0.2916667 -3.0085005 3.5918338 0.9991711
## d10-none 0.8333333 -2.4668338 4.1335005 0.9550964
## d15-none 1.8750000 -1.4251671 5.1751671 0.5119644
## d05-d0 3.2916667 1.2044577 5.3788756 0.0003011
## d10-d0 3.8333333 1.7461244 5.9205423 0.0000184
## d15-d0 4.8750000 2.7877910 6.9622090 0.0000000
## d10-d05 0.5416667 -1.5455423 2.6288756 0.9505498
## d15-d05 1.5833333 -0.5038756 3.6705423 0.2236150
## d15-d10 1.0416667 -1.0455423 3.1288756 0.6350673
##
## $`Treatment:Bti_application`
## diff lwr upr p adj
## 05%:none-Ctrl:none NA NA NA NA
## 10%:none-Ctrl:none NA NA NA NA
## 25%:none-Ctrl:none NA NA NA NA
## 50%:none-Ctrl:none NA NA NA NA
## Ctrl:d0-Ctrl:none NA NA NA NA
## 05%:d0-Ctrl:none -5.000000e-01 -6.1641633 5.1641633 1.0000000
## 10%:d0-Ctrl:none -1.833333e+00 -7.4974966 3.8308300 0.9999340
## 25%:d0-Ctrl:none -4.666667e+00 -10.3308300 0.9974966 0.2656295
## 50%:d0-Ctrl:none -6.500000e+00 -12.1641633 -0.8358367 0.0085045
## Ctrl:d05-Ctrl:none NA NA NA NA
## 05%:d05-Ctrl:none -1.333333e+00 -6.9974966 4.3308300 0.9999998
## 10%:d05-Ctrl:none 2.833333e+00 -2.8308300 8.4974966 0.9689165
## 25%:d05-Ctrl:none 0.000000e+00 -5.6641633 5.6641633 1.0000000
## 50%:d05-Ctrl:none -1.833333e+00 -7.4974966 3.8308300 0.9999340
## Ctrl:d10-Ctrl:none NA NA NA NA
## 05%:d10-Ctrl:none -3.333333e-01 -5.9974966 5.3308300 1.0000000
## 10%:d10-Ctrl:none 1.333333e+00 -4.3308300 6.9974966 0.9999998
## 25%:d10-Ctrl:none 1.166667e+00 -4.4974966 6.8308300 1.0000000
## 50%:d10-Ctrl:none -3.333333e-01 -5.9974966 5.3308300 1.0000000
## Ctrl:d15-Ctrl:none NA NA NA NA
## 05%:d15-Ctrl:none 2.166667e+00 -3.4974966 7.8308300 0.9990317
## 10%:d15-Ctrl:none 3.166667e+00 -2.4974966 8.8308300 0.9080917
## 25%:d15-Ctrl:none -1.333333e+00 -6.9974966 4.3308300 0.9999998
## 50%:d15-Ctrl:none 2.000000e+00 -3.6641633 7.6641633 0.9997204
## 10%:none-05%:none NA NA NA NA
## 25%:none-05%:none NA NA NA NA
## 50%:none-05%:none NA NA NA NA
## Ctrl:d0-05%:none NA NA NA NA
## 05%:d0-05%:none NA NA NA NA
## 10%:d0-05%:none NA NA NA NA
## 25%:d0-05%:none NA NA NA NA
## 50%:d0-05%:none NA NA NA NA
## Ctrl:d05-05%:none NA NA NA NA
## 05%:d05-05%:none NA NA NA NA
## 10%:d05-05%:none NA NA NA NA
## 25%:d05-05%:none NA NA NA NA
## 50%:d05-05%:none NA NA NA NA
## Ctrl:d10-05%:none NA NA NA NA
## 05%:d10-05%:none NA NA NA NA
## 10%:d10-05%:none NA NA NA NA
## 25%:d10-05%:none NA NA NA NA
## 50%:d10-05%:none NA NA NA NA
## Ctrl:d15-05%:none NA NA NA NA
## 05%:d15-05%:none NA NA NA NA
## 10%:d15-05%:none NA NA NA NA
## 25%:d15-05%:none NA NA NA NA
## 50%:d15-05%:none NA NA NA NA
## 25%:none-10%:none NA NA NA NA
## 50%:none-10%:none NA NA NA NA
## Ctrl:d0-10%:none NA NA NA NA
## 05%:d0-10%:none NA NA NA NA
## 10%:d0-10%:none NA NA NA NA
## 25%:d0-10%:none NA NA NA NA
## 50%:d0-10%:none NA NA NA NA
## Ctrl:d05-10%:none NA NA NA NA
## 05%:d05-10%:none NA NA NA NA
## 10%:d05-10%:none NA NA NA NA
## 25%:d05-10%:none NA NA NA NA
## 50%:d05-10%:none NA NA NA NA
## Ctrl:d10-10%:none NA NA NA NA
## 05%:d10-10%:none NA NA NA NA
## 10%:d10-10%:none NA NA NA NA
## 25%:d10-10%:none NA NA NA NA
## 50%:d10-10%:none NA NA NA NA
## Ctrl:d15-10%:none NA NA NA NA
## 05%:d15-10%:none NA NA NA NA
## 10%:d15-10%:none NA NA NA NA
## 25%:d15-10%:none NA NA NA NA
## 50%:d15-10%:none NA NA NA NA
## 50%:none-25%:none NA NA NA NA
## Ctrl:d0-25%:none NA NA NA NA
## 05%:d0-25%:none NA NA NA NA
## 10%:d0-25%:none NA NA NA NA
## 25%:d0-25%:none NA NA NA NA
## 50%:d0-25%:none NA NA NA NA
## Ctrl:d05-25%:none NA NA NA NA
## 05%:d05-25%:none NA NA NA NA
## 10%:d05-25%:none NA NA NA NA
## 25%:d05-25%:none NA NA NA NA
## 50%:d05-25%:none NA NA NA NA
## Ctrl:d10-25%:none NA NA NA NA
## 05%:d10-25%:none NA NA NA NA
## 10%:d10-25%:none NA NA NA NA
## 25%:d10-25%:none NA NA NA NA
## 50%:d10-25%:none NA NA NA NA
## Ctrl:d15-25%:none NA NA NA NA
## 05%:d15-25%:none NA NA NA NA
## 10%:d15-25%:none NA NA NA NA
## 25%:d15-25%:none NA NA NA NA
## 50%:d15-25%:none NA NA NA NA
## Ctrl:d0-50%:none NA NA NA NA
## 05%:d0-50%:none NA NA NA NA
## 10%:d0-50%:none NA NA NA NA
## 25%:d0-50%:none NA NA NA NA
## 50%:d0-50%:none NA NA NA NA
## Ctrl:d05-50%:none NA NA NA NA
## 05%:d05-50%:none NA NA NA NA
## 10%:d05-50%:none NA NA NA NA
## 25%:d05-50%:none NA NA NA NA
## 50%:d05-50%:none NA NA NA NA
## Ctrl:d10-50%:none NA NA NA NA
## 05%:d10-50%:none NA NA NA NA
## 10%:d10-50%:none NA NA NA NA
## 25%:d10-50%:none NA NA NA NA
## 50%:d10-50%:none NA NA NA NA
## Ctrl:d15-50%:none NA NA NA NA
## 05%:d15-50%:none NA NA NA NA
## 10%:d15-50%:none NA NA NA NA
## 25%:d15-50%:none NA NA NA NA
## 50%:d15-50%:none NA NA NA NA
## 05%:d0-Ctrl:d0 NA NA NA NA
## 10%:d0-Ctrl:d0 NA NA NA NA
## 25%:d0-Ctrl:d0 NA NA NA NA
## 50%:d0-Ctrl:d0 NA NA NA NA
## Ctrl:d05-Ctrl:d0 NA NA NA NA
## 05%:d05-Ctrl:d0 NA NA NA NA
## 10%:d05-Ctrl:d0 NA NA NA NA
## 25%:d05-Ctrl:d0 NA NA NA NA
## 50%:d05-Ctrl:d0 NA NA NA NA
## Ctrl:d10-Ctrl:d0 NA NA NA NA
## 05%:d10-Ctrl:d0 NA NA NA NA
## 10%:d10-Ctrl:d0 NA NA NA NA
## 25%:d10-Ctrl:d0 NA NA NA NA
## 50%:d10-Ctrl:d0 NA NA NA NA
## Ctrl:d15-Ctrl:d0 NA NA NA NA
## 05%:d15-Ctrl:d0 NA NA NA NA
## 10%:d15-Ctrl:d0 NA NA NA NA
## 25%:d15-Ctrl:d0 NA NA NA NA
## 50%:d15-Ctrl:d0 NA NA NA NA
## 10%:d0-05%:d0 -1.333333e+00 -6.9974966 4.3308300 0.9999998
## 25%:d0-05%:d0 -4.166667e+00 -9.8308300 1.4974966 0.4830424
## 50%:d0-05%:d0 -6.000000e+00 -11.6641633 -0.3358367 0.0253935
## Ctrl:d05-05%:d0 NA NA NA NA
## 05%:d05-05%:d0 -8.333333e-01 -6.4974966 4.8308300 1.0000000
## 10%:d05-05%:d0 3.333333e+00 -2.3308300 8.9974966 0.8594219
## 25%:d05-05%:d0 5.000000e-01 -5.1641633 6.1641633 1.0000000
## 50%:d05-05%:d0 -1.333333e+00 -6.9974966 4.3308300 0.9999998
## Ctrl:d10-05%:d0 NA NA NA NA
## 05%:d10-05%:d0 1.666667e-01 -5.4974966 5.8308300 1.0000000
## 10%:d10-05%:d0 1.833333e+00 -3.8308300 7.4974966 0.9999340
## 25%:d10-05%:d0 1.666667e+00 -3.9974966 7.3308300 0.9999877
## 50%:d10-05%:d0 1.666667e-01 -5.4974966 5.8308300 1.0000000
## Ctrl:d15-05%:d0 NA NA NA NA
## 05%:d15-05%:d0 2.666667e+00 -2.9974966 8.3308300 0.9842868
## 10%:d15-05%:d0 3.666667e+00 -1.9974966 9.3308300 0.7271670
## 25%:d15-05%:d0 -8.333333e-01 -6.4974966 4.8308300 1.0000000
## 50%:d15-05%:d0 2.500000e+00 -3.1641633 8.1641633 0.9928967
## 25%:d0-10%:d0 -2.833333e+00 -8.4974966 2.8308300 0.9689165
## 50%:d0-10%:d0 -4.666667e+00 -10.3308300 0.9974966 0.2656295
## Ctrl:d05-10%:d0 NA NA NA NA
## 05%:d05-10%:d0 5.000000e-01 -5.1641633 6.1641633 1.0000000
## 10%:d05-10%:d0 4.666667e+00 -0.9974966 10.3308300 0.2656295
## 25%:d05-10%:d0 1.833333e+00 -3.8308300 7.4974966 0.9999340
## 50%:d05-10%:d0 -8.881784e-16 -5.6641633 5.6641633 1.0000000
## Ctrl:d10-10%:d0 NA NA NA NA
## 05%:d10-10%:d0 1.500000e+00 -4.1641633 7.1641633 0.9999983
## 10%:d10-10%:d0 3.166667e+00 -2.4974966 8.8308300 0.9080917
## 25%:d10-10%:d0 3.000000e+00 -2.6641633 8.6641633 0.9442242
## 50%:d10-10%:d0 1.500000e+00 -4.1641633 7.1641633 0.9999983
## Ctrl:d15-10%:d0 NA NA NA NA
## 05%:d15-10%:d0 4.000000e+00 -1.6641633 9.6641633 0.5656846
## 10%:d15-10%:d0 5.000000e+00 -0.6641633 10.6641633 0.1621134
## 25%:d15-10%:d0 5.000000e-01 -5.1641633 6.1641633 1.0000000
## 50%:d15-10%:d0 3.833333e+00 -1.8308300 9.4974966 0.6483288
## 50%:d0-25%:d0 -1.833333e+00 -7.4974966 3.8308300 0.9999340
## Ctrl:d05-25%:d0 NA NA NA NA
## 05%:d05-25%:d0 3.333333e+00 -2.3308300 8.9974966 0.8594219
## 10%:d05-25%:d0 7.500000e+00 1.8358367 13.1641633 0.0007467
## 25%:d05-25%:d0 4.666667e+00 -0.9974966 10.3308300 0.2656295
## 50%:d05-25%:d0 2.833333e+00 -2.8308300 8.4974966 0.9689165
## Ctrl:d10-25%:d0 NA NA NA NA
## 05%:d10-25%:d0 4.333333e+00 -1.3308300 9.9974966 0.4038329
## 10%:d10-25%:d0 6.000000e+00 0.3358367 11.6641633 0.0253935
## 25%:d10-25%:d0 5.833333e+00 0.1691700 11.4974966 0.0357650
## 50%:d10-25%:d0 4.333333e+00 -1.3308300 9.9974966 0.4038329
## Ctrl:d15-25%:d0 NA NA NA NA
## 05%:d15-25%:d0 6.833333e+00 1.1691700 12.4974966 0.0039054
## 10%:d15-25%:d0 7.833333e+00 2.1691700 13.4974966 0.0003131
## 25%:d15-25%:d0 3.333333e+00 -2.3308300 8.9974966 0.8594219
## 50%:d15-25%:d0 6.666667e+00 1.0025034 12.3308300 0.0057891
## Ctrl:d05-50%:d0 NA NA NA NA
## 05%:d05-50%:d0 5.166667e+00 -0.4974966 10.8308300 0.1234399
## 10%:d05-50%:d0 9.333333e+00 3.6691700 14.9974966 0.0000048
## 25%:d05-50%:d0 6.500000e+00 0.8358367 12.1641633 0.0085045
## 50%:d05-50%:d0 4.666667e+00 -0.9974966 10.3308300 0.2656295
## Ctrl:d10-50%:d0 NA NA NA NA
## 05%:d10-50%:d0 6.166667e+00 0.5025034 11.8308300 0.0178228
## 10%:d10-50%:d0 7.833333e+00 2.1691700 13.4974966 0.0003131
## 25%:d10-50%:d0 7.666667e+00 2.0025034 13.3308300 0.0004850
## 50%:d10-50%:d0 6.166667e+00 0.5025034 11.8308300 0.0178228
## Ctrl:d15-50%:d0 NA NA NA NA
## 05%:d15-50%:d0 8.666667e+00 3.0025034 14.3308300 0.0000323
## 10%:d15-50%:d0 9.666667e+00 4.0025034 15.3308300 0.0000018
## 25%:d15-50%:d0 5.166667e+00 -0.4974966 10.8308300 0.1234399
## 50%:d15-50%:d0 8.500000e+00 2.8358367 14.1641633 0.0000514
## 05%:d05-Ctrl:d05 NA NA NA NA
## 10%:d05-Ctrl:d05 NA NA NA NA
## 25%:d05-Ctrl:d05 NA NA NA NA
## 50%:d05-Ctrl:d05 NA NA NA NA
## Ctrl:d10-Ctrl:d05 NA NA NA NA
## 05%:d10-Ctrl:d05 NA NA NA NA
## 10%:d10-Ctrl:d05 NA NA NA NA
## 25%:d10-Ctrl:d05 NA NA NA NA
## 50%:d10-Ctrl:d05 NA NA NA NA
## Ctrl:d15-Ctrl:d05 NA NA NA NA
## 05%:d15-Ctrl:d05 NA NA NA NA
## 10%:d15-Ctrl:d05 NA NA NA NA
## 25%:d15-Ctrl:d05 NA NA NA NA
## 50%:d15-Ctrl:d05 NA NA NA NA
## 10%:d05-05%:d05 4.166667e+00 -1.4974966 9.8308300 0.4830424
## 25%:d05-05%:d05 1.333333e+00 -4.3308300 6.9974966 0.9999998
## 50%:d05-05%:d05 -5.000000e-01 -6.1641633 5.1641633 1.0000000
## Ctrl:d10-05%:d05 NA NA NA NA
## 05%:d10-05%:d05 1.000000e+00 -4.6641633 6.6641633 1.0000000
## 10%:d10-05%:d05 2.666667e+00 -2.9974966 8.3308300 0.9842868
## 25%:d10-05%:d05 2.500000e+00 -3.1641633 8.1641633 0.9928967
## 50%:d10-05%:d05 1.000000e+00 -4.6641633 6.6641633 1.0000000
## Ctrl:d15-05%:d05 NA NA NA NA
## 05%:d15-05%:d05 3.500000e+00 -2.1641633 9.1641633 0.7985224
## 10%:d15-05%:d05 4.500000e+00 -1.1641633 10.1641633 0.3307587
## 25%:d15-05%:d05 -4.440892e-15 -5.6641633 5.6641633 1.0000000
## 50%:d15-05%:d05 3.333333e+00 -2.3308300 8.9974966 0.8594219
## 25%:d05-10%:d05 -2.833333e+00 -8.4974966 2.8308300 0.9689165
## 50%:d05-10%:d05 -4.666667e+00 -10.3308300 0.9974966 0.2656295
## Ctrl:d10-10%:d05 NA NA NA NA
## 05%:d10-10%:d05 -3.166667e+00 -8.8308300 2.4974966 0.9080917
## 10%:d10-10%:d05 -1.500000e+00 -7.1641633 4.1641633 0.9999983
## 25%:d10-10%:d05 -1.666667e+00 -7.3308300 3.9974966 0.9999877
## 50%:d10-10%:d05 -3.166667e+00 -8.8308300 2.4974966 0.9080917
## Ctrl:d15-10%:d05 NA NA NA NA
## 05%:d15-10%:d05 -6.666667e-01 -6.3308300 4.9974966 1.0000000
## 10%:d15-10%:d05 3.333333e-01 -5.3308300 5.9974966 1.0000000
## 25%:d15-10%:d05 -4.166667e+00 -9.8308300 1.4974966 0.4830424
## 50%:d15-10%:d05 -8.333333e-01 -6.4974966 4.8308300 1.0000000
## 50%:d05-25%:d05 -1.833333e+00 -7.4974966 3.8308300 0.9999340
## Ctrl:d10-25%:d05 NA NA NA NA
## 05%:d10-25%:d05 -3.333333e-01 -5.9974966 5.3308300 1.0000000
## 10%:d10-25%:d05 1.333333e+00 -4.3308300 6.9974966 0.9999998
## 25%:d10-25%:d05 1.166667e+00 -4.4974966 6.8308300 1.0000000
## 50%:d10-25%:d05 -3.333333e-01 -5.9974966 5.3308300 1.0000000
## Ctrl:d15-25%:d05 NA NA NA NA
## 05%:d15-25%:d05 2.166667e+00 -3.4974966 7.8308300 0.9990317
## 10%:d15-25%:d05 3.166667e+00 -2.4974966 8.8308300 0.9080917
## 25%:d15-25%:d05 -1.333333e+00 -6.9974966 4.3308300 0.9999998
## 50%:d15-25%:d05 2.000000e+00 -3.6641633 7.6641633 0.9997204
## Ctrl:d10-50%:d05 NA NA NA NA
## 05%:d10-50%:d05 1.500000e+00 -4.1641633 7.1641633 0.9999983
## 10%:d10-50%:d05 3.166667e+00 -2.4974966 8.8308300 0.9080917
## 25%:d10-50%:d05 3.000000e+00 -2.6641633 8.6641633 0.9442242
## 50%:d10-50%:d05 1.500000e+00 -4.1641633 7.1641633 0.9999983
## Ctrl:d15-50%:d05 NA NA NA NA
## 05%:d15-50%:d05 4.000000e+00 -1.6641633 9.6641633 0.5656846
## 10%:d15-50%:d05 5.000000e+00 -0.6641633 10.6641633 0.1621134
## 25%:d15-50%:d05 5.000000e-01 -5.1641633 6.1641633 1.0000000
## 50%:d15-50%:d05 3.833333e+00 -1.8308300 9.4974966 0.6483288
## 05%:d10-Ctrl:d10 NA NA NA NA
## 10%:d10-Ctrl:d10 NA NA NA NA
## 25%:d10-Ctrl:d10 NA NA NA NA
## 50%:d10-Ctrl:d10 NA NA NA NA
## Ctrl:d15-Ctrl:d10 NA NA NA NA
## 05%:d15-Ctrl:d10 NA NA NA NA
## 10%:d15-Ctrl:d10 NA NA NA NA
## 25%:d15-Ctrl:d10 NA NA NA NA
## 50%:d15-Ctrl:d10 NA NA NA NA
## 10%:d10-05%:d10 1.666667e+00 -3.9974966 7.3308300 0.9999877
## 25%:d10-05%:d10 1.500000e+00 -4.1641633 7.1641633 0.9999983
## 50%:d10-05%:d10 0.000000e+00 -5.6641633 5.6641633 1.0000000
## Ctrl:d15-05%:d10 NA NA NA NA
## 05%:d15-05%:d10 2.500000e+00 -3.1641633 8.1641633 0.9928967
## 10%:d15-05%:d10 3.500000e+00 -2.1641633 9.1641633 0.7985224
## 25%:d15-05%:d10 -1.000000e+00 -6.6641633 4.6641633 1.0000000
## 50%:d15-05%:d10 2.333333e+00 -3.3308300 7.9974966 0.9971758
## 25%:d10-10%:d10 -1.666667e-01 -5.8308300 5.4974966 1.0000000
## 50%:d10-10%:d10 -1.666667e+00 -7.3308300 3.9974966 0.9999877
## Ctrl:d15-10%:d10 NA NA NA NA
## 05%:d15-10%:d10 8.333333e-01 -4.8308300 6.4974966 1.0000000
## 10%:d15-10%:d10 1.833333e+00 -3.8308300 7.4974966 0.9999340
## 25%:d15-10%:d10 -2.666667e+00 -8.3308300 2.9974966 0.9842868
## 50%:d15-10%:d10 6.666667e-01 -4.9974966 6.3308300 1.0000000
## 50%:d10-25%:d10 -1.500000e+00 -7.1641633 4.1641633 0.9999983
## Ctrl:d15-25%:d10 NA NA NA NA
## 05%:d15-25%:d10 1.000000e+00 -4.6641633 6.6641633 1.0000000
## 10%:d15-25%:d10 2.000000e+00 -3.6641633 7.6641633 0.9997204
## 25%:d15-25%:d10 -2.500000e+00 -8.1641633 3.1641633 0.9928967
## 50%:d15-25%:d10 8.333333e-01 -4.8308300 6.4974966 1.0000000
## Ctrl:d15-50%:d10 NA NA NA NA
## 05%:d15-50%:d10 2.500000e+00 -3.1641633 8.1641633 0.9928967
## 10%:d15-50%:d10 3.500000e+00 -2.1641633 9.1641633 0.7985224
## 25%:d15-50%:d10 -1.000000e+00 -6.6641633 4.6641633 1.0000000
## 50%:d15-50%:d10 2.333333e+00 -3.3308300 7.9974966 0.9971758
## 05%:d15-Ctrl:d15 NA NA NA NA
## 10%:d15-Ctrl:d15 NA NA NA NA
## 25%:d15-Ctrl:d15 NA NA NA NA
## 50%:d15-Ctrl:d15 NA NA NA NA
## 10%:d15-05%:d15 1.000000e+00 -4.6641633 6.6641633 1.0000000
## 25%:d15-05%:d15 -3.500000e+00 -9.1641633 2.1641633 0.7985224
## 50%:d15-05%:d15 -1.666667e-01 -5.8308300 5.4974966 1.0000000
## 25%:d15-10%:d15 -4.500000e+00 -10.1641633 1.1641633 0.3307587
## 50%:d15-10%:d15 -1.166667e+00 -6.8308300 4.4974966 1.0000000
## 50%:d15-25%:d15 3.333333e+00 -2.3308300 8.9974966 0.8594219aov_Treatment_appl_F <- aov(Percent_Ind_F ~ Treatment * Bti_application, data = data[Day == 17])
summary(aov_Treatment_appl_F)## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 4 0.4099 0.10247 3.57 0.00968 **
## Bti_application 3 0.7122 0.23740 8.27 6.87e-05 ***
## Treatment:Bti_application 9 0.1964 0.02182 0.76 0.65323
## Residuals 85 2.4400 0.02871
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(aov_Treatment_appl_F)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Percent_Ind_F ~ Treatment * Bti_application, data = data[Day == 17])
##
## $Treatment
## diff lwr upr p adj
## 05%-Ctrl 0.060416667 -0.1551258 0.27595913 0.9353712
## 10%-Ctrl -0.025000000 -0.2405425 0.19054246 0.9975928
## 25%-Ctrl -0.002083333 -0.2176258 0.21345913 0.9999999
## 50%-Ctrl -0.120833333 -0.3363758 0.09470913 0.5253489
## 10%-05% -0.085416667 -0.2217377 0.05090435 0.4116047
## 25%-05% -0.062500000 -0.1988210 0.07382102 0.7054102
## 50%-05% -0.181250000 -0.3175710 -0.04492898 0.0033595
## 25%-10% 0.022916667 -0.1134044 0.15923769 0.9899497
## 50%-10% -0.095833333 -0.2321544 0.04048769 0.2947714
## 50%-25% -0.118750000 -0.2550710 0.01757102 0.1178494
##
## $Bti_application
## diff lwr upr p adj
## d0-none -0.14270833 -0.35825079 0.07283413 0.3548422
## d05-none 0.01354167 -0.20200079 0.22908413 0.9997852
## d10-none 0.04479167 -0.17075079 0.26033413 0.9777932
## d15-none 0.08437500 -0.13116746 0.29991746 0.8106739
## d05-d0 0.15625000 0.01992898 0.29257102 0.0163933
## d10-d0 0.18750000 0.05117898 0.32382102 0.0021964
## d15-d0 0.22708333 0.09076231 0.36340435 0.0001189
## d10-d05 0.03125000 -0.10507102 0.16757102 0.9682279
## d15-d05 0.07083333 -0.06548769 0.20715435 0.5985529
## d15-d10 0.03958333 -0.09673769 0.17590435 0.9270907
##
## $`Treatment:Bti_application`
## diff lwr upr p adj
## 05%:none-Ctrl:none NA NA NA NA
## 10%:none-Ctrl:none NA NA NA NA
## 25%:none-Ctrl:none NA NA NA NA
## 50%:none-Ctrl:none NA NA NA NA
## Ctrl:d0-Ctrl:none NA NA NA NA
## 05%:d0-Ctrl:none -5.000000e-02 -0.41994117 0.31994117 1.0000000
## 10%:d0-Ctrl:none -1.583333e-01 -0.52827450 0.21160784 0.9952492
## 25%:d0-Ctrl:none -1.583333e-01 -0.52827450 0.21160784 0.9952492
## 50%:d0-Ctrl:none -2.916667e-01 -0.66160784 0.07827450 0.3451924
## Ctrl:d05-Ctrl:none NA NA NA NA
## 05%:d05-Ctrl:none 1.166667e-01 -0.25327450 0.48660784 0.9999578
## 10%:d05-Ctrl:none -4.166667e-02 -0.41160784 0.32827450 1.0000000
## 25%:d05-Ctrl:none 5.000000e-02 -0.31994117 0.41994117 1.0000000
## 50%:d05-Ctrl:none -1.583333e-01 -0.52827450 0.21160784 0.9952492
## Ctrl:d10-Ctrl:none NA NA NA NA
## 05%:d10-Ctrl:none 1.333333e-01 -0.23660784 0.50327450 0.9996124
## 10%:d10-Ctrl:none 5.000000e-02 -0.31994117 0.41994117 1.0000000
## 25%:d10-Ctrl:none -6.661338e-16 -0.36994117 0.36994117 1.0000000
## 50%:d10-Ctrl:none -9.166667e-02 -0.46160784 0.27827450 0.9999995
## Ctrl:d15-Ctrl:none NA NA NA NA
## 05%:d15-Ctrl:none 4.166667e-02 -0.32827450 0.41160784 1.0000000
## 10%:d15-Ctrl:none 5.000000e-02 -0.31994117 0.41994117 1.0000000
## 25%:d15-Ctrl:none 1.000000e-01 -0.26994117 0.46994117 0.9999974
## 50%:d15-Ctrl:none 5.833333e-02 -0.31160784 0.42827450 1.0000000
## 10%:none-05%:none NA NA NA NA
## 25%:none-05%:none NA NA NA NA
## 50%:none-05%:none NA NA NA NA
## Ctrl:d0-05%:none NA NA NA NA
## 05%:d0-05%:none NA NA NA NA
## 10%:d0-05%:none NA NA NA NA
## 25%:d0-05%:none NA NA NA NA
## 50%:d0-05%:none NA NA NA NA
## Ctrl:d05-05%:none NA NA NA NA
## 05%:d05-05%:none NA NA NA NA
## 10%:d05-05%:none NA NA NA NA
## 25%:d05-05%:none NA NA NA NA
## 50%:d05-05%:none NA NA NA NA
## Ctrl:d10-05%:none NA NA NA NA
## 05%:d10-05%:none NA NA NA NA
## 10%:d10-05%:none NA NA NA NA
## 25%:d10-05%:none NA NA NA NA
## 50%:d10-05%:none NA NA NA NA
## Ctrl:d15-05%:none NA NA NA NA
## 05%:d15-05%:none NA NA NA NA
## 10%:d15-05%:none NA NA NA NA
## 25%:d15-05%:none NA NA NA NA
## 50%:d15-05%:none NA NA NA NA
## 25%:none-10%:none NA NA NA NA
## 50%:none-10%:none NA NA NA NA
## Ctrl:d0-10%:none NA NA NA NA
## 05%:d0-10%:none NA NA NA NA
## 10%:d0-10%:none NA NA NA NA
## 25%:d0-10%:none NA NA NA NA
## 50%:d0-10%:none NA NA NA NA
## Ctrl:d05-10%:none NA NA NA NA
## 05%:d05-10%:none NA NA NA NA
## 10%:d05-10%:none NA NA NA NA
## 25%:d05-10%:none NA NA NA NA
## 50%:d05-10%:none NA NA NA NA
## Ctrl:d10-10%:none NA NA NA NA
## 05%:d10-10%:none NA NA NA NA
## 10%:d10-10%:none NA NA NA NA
## 25%:d10-10%:none NA NA NA NA
## 50%:d10-10%:none NA NA NA NA
## Ctrl:d15-10%:none NA NA NA NA
## 05%:d15-10%:none NA NA NA NA
## 10%:d15-10%:none NA NA NA NA
## 25%:d15-10%:none NA NA NA NA
## 50%:d15-10%:none NA NA NA NA
## 50%:none-25%:none NA NA NA NA
## Ctrl:d0-25%:none NA NA NA NA
## 05%:d0-25%:none NA NA NA NA
## 10%:d0-25%:none NA NA NA NA
## 25%:d0-25%:none NA NA NA NA
## 50%:d0-25%:none NA NA NA NA
## Ctrl:d05-25%:none NA NA NA NA
## 05%:d05-25%:none NA NA NA NA
## 10%:d05-25%:none NA NA NA NA
## 25%:d05-25%:none NA NA NA NA
## 50%:d05-25%:none NA NA NA NA
## Ctrl:d10-25%:none NA NA NA NA
## 05%:d10-25%:none NA NA NA NA
## 10%:d10-25%:none NA NA NA NA
## 25%:d10-25%:none NA NA NA NA
## 50%:d10-25%:none NA NA NA NA
## Ctrl:d15-25%:none NA NA NA NA
## 05%:d15-25%:none NA NA NA NA
## 10%:d15-25%:none NA NA NA NA
## 25%:d15-25%:none NA NA NA NA
## 50%:d15-25%:none NA NA NA NA
## Ctrl:d0-50%:none NA NA NA NA
## 05%:d0-50%:none NA NA NA NA
## 10%:d0-50%:none NA NA NA NA
## 25%:d0-50%:none NA NA NA NA
## 50%:d0-50%:none NA NA NA NA
## Ctrl:d05-50%:none NA NA NA NA
## 05%:d05-50%:none NA NA NA NA
## 10%:d05-50%:none NA NA NA NA
## 25%:d05-50%:none NA NA NA NA
## 50%:d05-50%:none NA NA NA NA
## Ctrl:d10-50%:none NA NA NA NA
## 05%:d10-50%:none NA NA NA NA
## 10%:d10-50%:none NA NA NA NA
## 25%:d10-50%:none NA NA NA NA
## 50%:d10-50%:none NA NA NA NA
## Ctrl:d15-50%:none NA NA NA NA
## 05%:d15-50%:none NA NA NA NA
## 10%:d15-50%:none NA NA NA NA
## 25%:d15-50%:none NA NA NA NA
## 50%:d15-50%:none NA NA NA NA
## 05%:d0-Ctrl:d0 NA NA NA NA
## 10%:d0-Ctrl:d0 NA NA NA NA
## 25%:d0-Ctrl:d0 NA NA NA NA
## 50%:d0-Ctrl:d0 NA NA NA NA
## Ctrl:d05-Ctrl:d0 NA NA NA NA
## 05%:d05-Ctrl:d0 NA NA NA NA
## 10%:d05-Ctrl:d0 NA NA NA NA
## 25%:d05-Ctrl:d0 NA NA NA NA
## 50%:d05-Ctrl:d0 NA NA NA NA
## Ctrl:d10-Ctrl:d0 NA NA NA NA
## 05%:d10-Ctrl:d0 NA NA NA NA
## 10%:d10-Ctrl:d0 NA NA NA NA
## 25%:d10-Ctrl:d0 NA NA NA NA
## 50%:d10-Ctrl:d0 NA NA NA NA
## Ctrl:d15-Ctrl:d0 NA NA NA NA
## 05%:d15-Ctrl:d0 NA NA NA NA
## 10%:d15-Ctrl:d0 NA NA NA NA
## 25%:d15-Ctrl:d0 NA NA NA NA
## 50%:d15-Ctrl:d0 NA NA NA NA
## 10%:d0-05%:d0 -1.083333e-01 -0.47827450 0.26160784 0.9999887
## 25%:d0-05%:d0 -1.083333e-01 -0.47827450 0.26160784 0.9999887
## 50%:d0-05%:d0 -2.416667e-01 -0.61160784 0.12827450 0.7118154
## Ctrl:d05-05%:d0 NA NA NA NA
## 05%:d05-05%:d0 1.666667e-01 -0.20327450 0.53660784 0.9907891
## 10%:d05-05%:d0 8.333333e-03 -0.36160784 0.37827450 1.0000000
## 25%:d05-05%:d0 1.000000e-01 -0.26994117 0.46994117 0.9999974
## 50%:d05-05%:d0 -1.083333e-01 -0.47827450 0.26160784 0.9999887
## Ctrl:d10-05%:d0 NA NA NA NA
## 05%:d10-05%:d0 1.833333e-01 -0.18660784 0.55327450 0.9718999
## 10%:d10-05%:d0 1.000000e-01 -0.26994117 0.46994117 0.9999974
## 25%:d10-05%:d0 5.000000e-02 -0.31994117 0.41994117 1.0000000
## 50%:d10-05%:d0 -4.166667e-02 -0.41160784 0.32827450 1.0000000
## Ctrl:d15-05%:d0 NA NA NA NA
## 05%:d15-05%:d0 9.166667e-02 -0.27827450 0.46160784 0.9999995
## 10%:d15-05%:d0 1.000000e-01 -0.26994117 0.46994117 0.9999974
## 25%:d15-05%:d0 1.500000e-01 -0.21994117 0.51994117 0.9977384
## 50%:d15-05%:d0 1.083333e-01 -0.26160784 0.47827450 0.9999887
## 25%:d0-10%:d0 -1.665335e-16 -0.36994117 0.36994117 1.0000000
## 50%:d0-10%:d0 -1.333333e-01 -0.50327450 0.23660784 0.9996124
## Ctrl:d05-10%:d0 NA NA NA NA
## 05%:d05-10%:d0 2.750000e-01 -0.09494117 0.64494117 0.4617425
## 10%:d05-10%:d0 1.166667e-01 -0.25327450 0.48660784 0.9999578
## 25%:d05-10%:d0 2.083333e-01 -0.16160784 0.57827450 0.9020976
## 50%:d05-10%:d0 3.885781e-16 -0.36994117 0.36994117 1.0000000
## Ctrl:d10-10%:d0 NA NA NA NA
## 05%:d10-10%:d0 2.916667e-01 -0.07827450 0.66160784 0.3451924
## 10%:d10-10%:d0 2.083333e-01 -0.16160784 0.57827450 0.9020976
## 25%:d10-10%:d0 1.583333e-01 -0.21160784 0.52827450 0.9952492
## 50%:d10-10%:d0 6.666667e-02 -0.30327450 0.43660784 1.0000000
## Ctrl:d15-10%:d0 NA NA NA NA
## 05%:d15-10%:d0 2.000000e-01 -0.16994117 0.56994117 0.9321645
## 10%:d15-10%:d0 2.083333e-01 -0.16160784 0.57827450 0.9020976
## 25%:d15-10%:d0 2.583333e-01 -0.11160784 0.62827450 0.5879858
## 50%:d15-10%:d0 2.166667e-01 -0.15327450 0.58660784 0.8646261
## 50%:d0-25%:d0 -1.333333e-01 -0.50327450 0.23660784 0.9996124
## Ctrl:d05-25%:d0 NA NA NA NA
## 05%:d05-25%:d0 2.750000e-01 -0.09494117 0.64494117 0.4617425
## 10%:d05-25%:d0 1.166667e-01 -0.25327450 0.48660784 0.9999578
## 25%:d05-25%:d0 2.083333e-01 -0.16160784 0.57827450 0.9020976
## 50%:d05-25%:d0 5.551115e-16 -0.36994117 0.36994117 1.0000000
## Ctrl:d10-25%:d0 NA NA NA NA
## 05%:d10-25%:d0 2.916667e-01 -0.07827450 0.66160784 0.3451924
## 10%:d10-25%:d0 2.083333e-01 -0.16160784 0.57827450 0.9020976
## 25%:d10-25%:d0 1.583333e-01 -0.21160784 0.52827450 0.9952492
## 50%:d10-25%:d0 6.666667e-02 -0.30327450 0.43660784 1.0000000
## Ctrl:d15-25%:d0 NA NA NA NA
## 05%:d15-25%:d0 2.000000e-01 -0.16994117 0.56994117 0.9321645
## 10%:d15-25%:d0 2.083333e-01 -0.16160784 0.57827450 0.9020976
## 25%:d15-25%:d0 2.583333e-01 -0.11160784 0.62827450 0.5879858
## 50%:d15-25%:d0 2.166667e-01 -0.15327450 0.58660784 0.8646261
## Ctrl:d05-50%:d0 NA NA NA NA
## 05%:d05-50%:d0 4.083333e-01 0.03839216 0.77827450 0.0148058
## 10%:d05-50%:d0 2.500000e-01 -0.11994117 0.61994117 0.6510549
## 25%:d05-50%:d0 3.416667e-01 -0.02827450 0.71160784 0.1105915
## 50%:d05-50%:d0 1.333333e-01 -0.23660784 0.50327450 0.9996124
## Ctrl:d10-50%:d0 NA NA NA NA
## 05%:d10-50%:d0 4.250000e-01 0.05505883 0.79494117 0.0083666
## 10%:d10-50%:d0 3.416667e-01 -0.02827450 0.71160784 0.1105915
## 25%:d10-50%:d0 2.916667e-01 -0.07827450 0.66160784 0.3451924
## 50%:d10-50%:d0 2.000000e-01 -0.16994117 0.56994117 0.9321645
## Ctrl:d15-50%:d0 NA NA NA NA
## 05%:d15-50%:d0 3.333333e-01 -0.03660784 0.70327450 0.1370974
## 10%:d15-50%:d0 3.416667e-01 -0.02827450 0.71160784 0.1105915
## 25%:d15-50%:d0 3.916667e-01 0.02172550 0.76160784 0.0255638
## 50%:d15-50%:d0 3.500000e-01 -0.01994117 0.71994117 0.0884081
## 05%:d05-Ctrl:d05 NA NA NA NA
## 10%:d05-Ctrl:d05 NA NA NA NA
## 25%:d05-Ctrl:d05 NA NA NA NA
## 50%:d05-Ctrl:d05 NA NA NA NA
## Ctrl:d10-Ctrl:d05 NA NA NA NA
## 05%:d10-Ctrl:d05 NA NA NA NA
## 10%:d10-Ctrl:d05 NA NA NA NA
## 25%:d10-Ctrl:d05 NA NA NA NA
## 50%:d10-Ctrl:d05 NA NA NA NA
## Ctrl:d15-Ctrl:d05 NA NA NA NA
## 05%:d15-Ctrl:d05 NA NA NA NA
## 10%:d15-Ctrl:d05 NA NA NA NA
## 25%:d15-Ctrl:d05 NA NA NA NA
## 50%:d15-Ctrl:d05 NA NA NA NA
## 10%:d05-05%:d05 -1.583333e-01 -0.52827450 0.21160784 0.9952492
## 25%:d05-05%:d05 -6.666667e-02 -0.43660784 0.30327450 1.0000000
## 50%:d05-05%:d05 -2.750000e-01 -0.64494117 0.09494117 0.4617425
## Ctrl:d10-05%:d05 NA NA NA NA
## 05%:d10-05%:d05 1.666667e-02 -0.35327450 0.38660784 1.0000000
## 10%:d10-05%:d05 -6.666667e-02 -0.43660784 0.30327450 1.0000000
## 25%:d10-05%:d05 -1.166667e-01 -0.48660784 0.25327450 0.9999578
## 50%:d10-05%:d05 -2.083333e-01 -0.57827450 0.16160784 0.9020976
## Ctrl:d15-05%:d05 NA NA NA NA
## 05%:d15-05%:d05 -7.500000e-02 -0.44494117 0.29494117 1.0000000
## 10%:d15-05%:d05 -6.666667e-02 -0.43660784 0.30327450 1.0000000
## 25%:d15-05%:d05 -1.666667e-02 -0.38660784 0.35327450 1.0000000
## 50%:d15-05%:d05 -5.833333e-02 -0.42827450 0.31160784 1.0000000
## 25%:d05-10%:d05 9.166667e-02 -0.27827450 0.46160784 0.9999995
## 50%:d05-10%:d05 -1.166667e-01 -0.48660784 0.25327450 0.9999578
## Ctrl:d10-10%:d05 NA NA NA NA
## 05%:d10-10%:d05 1.750000e-01 -0.19494117 0.54494117 0.9833824
## 10%:d10-10%:d05 9.166667e-02 -0.27827450 0.46160784 0.9999995
## 25%:d10-10%:d05 4.166667e-02 -0.32827450 0.41160784 1.0000000
## 50%:d10-10%:d05 -5.000000e-02 -0.41994117 0.31994117 1.0000000
## Ctrl:d15-10%:d05 NA NA NA NA
## 05%:d15-10%:d05 8.333333e-02 -0.28660784 0.45327450 0.9999999
## 10%:d15-10%:d05 9.166667e-02 -0.27827450 0.46160784 0.9999995
## 25%:d15-10%:d05 1.416667e-01 -0.22827450 0.51160784 0.9990156
## 50%:d15-10%:d05 1.000000e-01 -0.26994117 0.46994117 0.9999974
## 50%:d05-25%:d05 -2.083333e-01 -0.57827450 0.16160784 0.9020976
## Ctrl:d10-25%:d05 NA NA NA NA
## 05%:d10-25%:d05 8.333333e-02 -0.28660784 0.45327450 0.9999999
## 10%:d10-25%:d05 -5.551115e-17 -0.36994117 0.36994117 1.0000000
## 25%:d10-25%:d05 -5.000000e-02 -0.41994117 0.31994117 1.0000000
## 50%:d10-25%:d05 -1.416667e-01 -0.51160784 0.22827450 0.9990156
## Ctrl:d15-25%:d05 NA NA NA NA
## 05%:d15-25%:d05 -8.333333e-03 -0.37827450 0.36160784 1.0000000
## 10%:d15-25%:d05 1.110223e-16 -0.36994117 0.36994117 1.0000000
## 25%:d15-25%:d05 5.000000e-02 -0.31994117 0.41994117 1.0000000
## 50%:d15-25%:d05 8.333333e-03 -0.36160784 0.37827450 1.0000000
## Ctrl:d10-50%:d05 NA NA NA NA
## 05%:d10-50%:d05 2.916667e-01 -0.07827450 0.66160784 0.3451924
## 10%:d10-50%:d05 2.083333e-01 -0.16160784 0.57827450 0.9020976
## 25%:d10-50%:d05 1.583333e-01 -0.21160784 0.52827450 0.9952492
## 50%:d10-50%:d05 6.666667e-02 -0.30327450 0.43660784 1.0000000
## Ctrl:d15-50%:d05 NA NA NA NA
## 05%:d15-50%:d05 2.000000e-01 -0.16994117 0.56994117 0.9321645
## 10%:d15-50%:d05 2.083333e-01 -0.16160784 0.57827450 0.9020976
## 25%:d15-50%:d05 2.583333e-01 -0.11160784 0.62827450 0.5879858
## 50%:d15-50%:d05 2.166667e-01 -0.15327450 0.58660784 0.8646261
## 05%:d10-Ctrl:d10 NA NA NA NA
## 10%:d10-Ctrl:d10 NA NA NA NA
## 25%:d10-Ctrl:d10 NA NA NA NA
## 50%:d10-Ctrl:d10 NA NA NA NA
## Ctrl:d15-Ctrl:d10 NA NA NA NA
## 05%:d15-Ctrl:d10 NA NA NA NA
## 10%:d15-Ctrl:d10 NA NA NA NA
## 25%:d15-Ctrl:d10 NA NA NA NA
## 50%:d15-Ctrl:d10 NA NA NA NA
## 10%:d10-05%:d10 -8.333333e-02 -0.45327450 0.28660784 0.9999999
## 25%:d10-05%:d10 -1.333333e-01 -0.50327450 0.23660784 0.9996124
## 50%:d10-05%:d10 -2.250000e-01 -0.59494117 0.14494117 0.8199012
## Ctrl:d15-05%:d10 NA NA NA NA
## 05%:d15-05%:d10 -9.166667e-02 -0.46160784 0.27827450 0.9999995
## 10%:d15-05%:d10 -8.333333e-02 -0.45327450 0.28660784 0.9999999
## 25%:d15-05%:d10 -3.333333e-02 -0.40327450 0.33660784 1.0000000
## 50%:d15-05%:d10 -7.500000e-02 -0.44494117 0.29494117 1.0000000
## 25%:d10-10%:d10 -5.000000e-02 -0.41994117 0.31994117 1.0000000
## 50%:d10-10%:d10 -1.416667e-01 -0.51160784 0.22827450 0.9990156
## Ctrl:d15-10%:d10 NA NA NA NA
## 05%:d15-10%:d10 -8.333333e-03 -0.37827450 0.36160784 1.0000000
## 10%:d15-10%:d10 1.665335e-16 -0.36994117 0.36994117 1.0000000
## 25%:d15-10%:d10 5.000000e-02 -0.31994117 0.41994117 1.0000000
## 50%:d15-10%:d10 8.333333e-03 -0.36160784 0.37827450 1.0000000
## 50%:d10-25%:d10 -9.166667e-02 -0.46160784 0.27827450 0.9999995
## Ctrl:d15-25%:d10 NA NA NA NA
## 05%:d15-25%:d10 4.166667e-02 -0.32827450 0.41160784 1.0000000
## 10%:d15-25%:d10 5.000000e-02 -0.31994117 0.41994117 1.0000000
## 25%:d15-25%:d10 1.000000e-01 -0.26994117 0.46994117 0.9999974
## 50%:d15-25%:d10 5.833333e-02 -0.31160784 0.42827450 1.0000000
## Ctrl:d15-50%:d10 NA NA NA NA
## 05%:d15-50%:d10 1.333333e-01 -0.23660784 0.50327450 0.9996124
## 10%:d15-50%:d10 1.416667e-01 -0.22827450 0.51160784 0.9990156
## 25%:d15-50%:d10 1.916667e-01 -0.17827450 0.56160784 0.9551763
## 50%:d15-50%:d10 1.500000e-01 -0.21994117 0.51994117 0.9977384
## 05%:d15-Ctrl:d15 NA NA NA NA
## 10%:d15-Ctrl:d15 NA NA NA NA
## 25%:d15-Ctrl:d15 NA NA NA NA
## 50%:d15-Ctrl:d15 NA NA NA NA
## 10%:d15-05%:d15 8.333333e-03 -0.36160784 0.37827450 1.0000000
## 25%:d15-05%:d15 5.833333e-02 -0.31160784 0.42827450 1.0000000
## 50%:d15-05%:d15 1.666667e-02 -0.35327450 0.38660784 1.0000000
## 25%:d15-10%:d15 5.000000e-02 -0.31994117 0.41994117 1.0000000
## 50%:d15-10%:d15 8.333333e-03 -0.36160784 0.37827450 1.0000000
## 50%:d15-25%:d15 -4.166667e-02 -0.41160784 0.32827450 1.0000000plot(aov_Treatment_appl_F)
#Larval_stage x Bti_application
aov_Larval_stage_appl <- aov(Ind ~ Larval_stage * Bti_application, data = data[Day == 17])
summary(aov_Larval_stage_appl)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 145.9 145.92 7.271 0.00833 **
## Bti_application 4 1210.2 302.55 15.075 1.63e-09 ***
## Larval_stage:Bti_application 4 314.6 78.64 3.919 0.00554 **
## Residuals 92 1846.3 20.07
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_appl) 
TukeyHSD(aov_Larval_stage_appl)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Ind ~ Larval_stage * Bti_application, data = data[Day == 17])
##
## $Larval_stage
## diff lwr upr p adj
## Lx-L1 2.392157 0.6302247 4.154089 0.0083337
##
## $Bti_application
## diff lwr upr p adj
## d0-none -6.6666667 -12.3564298 -0.9769035 0.0132185
## d05-none -0.2500000 -5.9397631 5.4397631 0.9999486
## d10-none 0.9166667 -4.7730965 6.6064298 0.9915144
## d15-none 2.7500000 -2.9397631 8.4397631 0.6640318
## d05-d0 6.4166667 2.8181445 10.0151888 0.0000310
## d10-d0 7.5833333 3.9848112 11.1818555 0.0000007
## d15-d0 9.4166667 5.8181445 13.0151888 0.0000000
## d10-d05 1.1666667 -2.4318555 4.7651888 0.8953828
## d15-d05 3.0000000 -0.5985222 6.5985222 0.1479713
## d15-d10 1.8333333 -1.7651888 5.4318555 0.6179717
##
## $`Larval_stage:Bti_application`
## diff lwr upr p adj
## Lx:none-L1:none -2.66666667 -14.5275234 9.1941900 0.9992462
## L1:d0-L1:none -12.25000000 -21.6268306 -2.8731694 0.0020984
## Lx:d0-L1:none -3.75000000 -13.1268306 5.6268306 0.9519670
## L1:d05-L1:none -1.91666667 -11.2934972 7.4601639 0.9996510
## Lx:d05-L1:none -1.25000000 -10.6268306 8.1268306 0.9999905
## L1:d10-L1:none -1.16666667 -10.5434972 8.2101639 0.9999948
## Lx:d10-L1:none 0.33333333 -9.0434972 9.7101639 1.0000000
## L1:d15-L1:none 1.33333333 -8.0434972 10.7101639 0.9999834
## Lx:d15-L1:none 1.50000000 -7.8768306 10.8768306 0.9999548
## L1:d0-Lx:none -9.58333333 -18.9601639 -0.2065028 0.0409760
## Lx:d0-Lx:none -1.08333333 -10.4601639 8.2934972 0.9999973
## L1:d05-Lx:none 0.75000000 -8.6268306 10.1268306 0.9999999
## Lx:d05-Lx:none 1.41666667 -7.9601639 10.7934972 0.9999722
## L1:d10-Lx:none 1.50000000 -7.8768306 10.8768306 0.9999548
## Lx:d10-Lx:none 3.00000000 -6.3768306 12.3768306 0.9890651
## L1:d15-Lx:none 4.00000000 -5.3768306 13.3768306 0.9293389
## Lx:d15-Lx:none 4.16666667 -5.2101639 13.5434972 0.9108097
## Lx:d0-L1:d0 8.50000000 2.5695716 14.4304284 0.0004574
## L1:d05-L1:d0 10.33333333 4.4029050 16.2637617 0.0000077
## Lx:d05-L1:d0 11.00000000 5.0695716 16.9304284 0.0000016
## L1:d10-L1:d0 11.08333333 5.1529050 17.0137617 0.0000013
## Lx:d10-L1:d0 12.58333333 6.6529050 18.5137617 0.0000000
## L1:d15-L1:d0 13.58333333 7.6529050 19.5137617 0.0000000
## Lx:d15-L1:d0 13.75000000 7.8195716 19.6804284 0.0000000
## L1:d05-Lx:d0 1.83333333 -4.0970950 7.7637617 0.9914391
## Lx:d05-Lx:d0 2.50000000 -3.4304284 8.4304284 0.9340714
## L1:d10-Lx:d0 2.58333333 -3.3470950 8.5137617 0.9202868
## Lx:d10-Lx:d0 4.08333333 -1.8470950 10.0137617 0.4426119
## L1:d15-Lx:d0 5.08333333 -0.8470950 11.0137617 0.1591417
## Lx:d15-Lx:d0 5.25000000 -0.6804284 11.1804284 0.1292050
## Lx:d05-L1:d05 0.66666667 -5.2637617 6.5970950 0.9999978
## L1:d10-L1:d05 0.75000000 -5.1804284 6.6804284 0.9999940
## Lx:d10-L1:d05 2.25000000 -3.6804284 8.1804284 0.9654744
## L1:d15-L1:d05 3.25000000 -2.6804284 9.1804284 0.7475995
## Lx:d15-L1:d05 3.41666667 -2.5137617 9.3470950 0.6899884
## L1:d10-Lx:d05 0.08333333 -5.8470950 6.0137617 1.0000000
## Lx:d10-Lx:d05 1.58333333 -4.3470950 7.5137617 0.9971113
## L1:d15-Lx:d05 2.58333333 -3.3470950 8.5137617 0.9202868
## Lx:d15-Lx:d05 2.75000000 -3.1804284 8.6804284 0.8873772
## Lx:d10-L1:d10 1.50000000 -4.4304284 7.4304284 0.9980933
## L1:d15-L1:d10 2.50000000 -3.4304284 8.4304284 0.9340714
## Lx:d15-L1:d10 2.66666667 -3.2637617 8.5970950 0.9047314
## L1:d15-Lx:d10 1.00000000 -4.9304284 6.9304284 0.9999294
## Lx:d15-Lx:d10 1.16666667 -4.7637617 7.0970950 0.9997450
## Lx:d15-L1:d15 0.16666667 -5.7637617 6.0970950 1.0000000aov_Larval_stage_appl_M <- aov(M ~ Larval_stage * Bti_application, data = data[Day == 17])
summary(aov_Larval_stage_appl_M)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 7.7 7.69 0.872 0.353
## Bti_application 4 319.9 79.97 9.075 3.18e-06 ***
## Larval_stage:Bti_application 4 49.1 12.27 1.392 0.243
## Residuals 92 810.7 8.81
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_appl_M)
TukeyHSD(aov_Larval_stage_appl_M)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = M ~ Larval_stage * Bti_application, data = data[Day == 17])
##
## $Larval_stage
## diff lwr upr p adj
## Lx-L1 0.5490196 -0.6184761 1.716515 0.3527667
##
## $Bti_application
## diff lwr upr p adj
## d0-none -3.37500000 -7.1451644 0.3951644 0.1015966
## d05-none -0.08333333 -3.8534977 3.6868311 0.9999967
## d10-none 0.45833333 -3.3118311 4.2284977 0.9971322
## d15-none 1.50000000 -2.2701644 5.2701644 0.8024087
## d05-d0 3.29166667 0.9072053 5.6761280 0.0020519
## d10-d0 3.83333333 1.4488720 6.2177947 0.0002097
## d15-d0 4.87500000 2.4905387 7.2594613 0.0000015
## d10-d05 0.54166667 -1.8427947 2.9261280 0.9694729
## d15-d05 1.58333333 -0.8011280 3.9677947 0.3529898
## d15-d10 1.04166667 -1.3427947 3.4261280 0.7423559
##
## $`Larval_stage:Bti_application`
## diff lwr upr p adj
## Lx:none-L1:none -2.666667e+00 -10.5259358 5.1926025 0.9835350
## L1:d0-L1:none -6.083333e+00 -12.2966311 0.1299645 0.0601157
## Lx:d0-L1:none -3.333333e+00 -9.5466311 2.8799645 0.7699452
## L1:d05-L1:none -1.583333e+00 -7.7966311 4.6299645 0.9979798
## Lx:d05-L1:none -1.250000e+00 -7.4632978 4.9632978 0.9996935
## L1:d10-L1:none -8.333333e-01 -7.0466311 5.3799645 0.9999900
## Lx:d10-L1:none -9.166667e-01 -7.1299645 5.2966311 0.9999773
## L1:d15-L1:none 1.666667e-01 -6.0466311 6.3799645 1.0000000
## Lx:d15-L1:none 1.666667e-01 -6.0466311 6.3799645 1.0000000
## L1:d0-Lx:none -3.416667e+00 -9.6299645 2.7966311 0.7438898
## Lx:d0-Lx:none -6.666667e-01 -6.8799645 5.5466311 0.9999986
## L1:d05-Lx:none 1.083333e+00 -5.1299645 7.2966311 0.9999065
## Lx:d05-Lx:none 1.416667e+00 -4.7966311 7.6299645 0.9991568
## L1:d10-Lx:none 1.833333e+00 -4.3799645 8.0466311 0.9938966
## Lx:d10-Lx:none 1.750000e+00 -4.4632978 7.9632978 0.9956751
## L1:d15-Lx:none 2.833333e+00 -3.3799645 9.0466311 0.8971090
## Lx:d15-Lx:none 2.833333e+00 -3.3799645 9.0466311 0.8971090
## Lx:d0-L1:d0 2.750000e+00 -1.1796346 6.6796346 0.4187641
## L1:d05-L1:d0 4.500000e+00 0.5703654 8.4296346 0.0123273
## Lx:d05-L1:d0 4.833333e+00 0.9036988 8.7629679 0.0049817
## L1:d10-L1:d0 5.250000e+00 1.3203654 9.1796346 0.0014848
## Lx:d10-L1:d0 5.166667e+00 1.2370321 9.0963012 0.0019037
## L1:d15-L1:d0 6.250000e+00 2.3203654 10.1796346 0.0000608
## Lx:d15-L1:d0 6.250000e+00 2.3203654 10.1796346 0.0000608
## L1:d05-Lx:d0 1.750000e+00 -2.1796346 5.6796346 0.9097093
## Lx:d05-Lx:d0 2.083333e+00 -1.8463012 6.0129679 0.7818185
## L1:d10-Lx:d0 2.500000e+00 -1.4296346 6.4296346 0.5578531
## Lx:d10-Lx:d0 2.416667e+00 -1.5129679 6.3463012 0.6052334
## L1:d15-Lx:d0 3.500000e+00 -0.4296346 7.4296346 0.1239889
## Lx:d15-Lx:d0 3.500000e+00 -0.4296346 7.4296346 0.1239889
## Lx:d05-L1:d05 3.333333e-01 -3.5963012 4.2629679 0.9999998
## L1:d10-L1:d05 7.500000e-01 -3.1796346 4.6796346 0.9998014
## Lx:d10-L1:d05 6.666667e-01 -3.2629679 4.5963012 0.9999257
## L1:d15-L1:d05 1.750000e+00 -2.1796346 5.6796346 0.9097093
## Lx:d15-L1:d05 1.750000e+00 -2.1796346 5.6796346 0.9097093
## L1:d10-Lx:d05 4.166667e-01 -3.5129679 4.3463012 0.9999987
## Lx:d10-Lx:d05 3.333333e-01 -3.5963012 4.2629679 0.9999998
## L1:d15-Lx:d05 1.416667e+00 -2.5129679 5.3463012 0.9752310
## Lx:d15-Lx:d05 1.416667e+00 -2.5129679 5.3463012 0.9752310
## Lx:d10-L1:d10 -8.333333e-02 -4.0129679 3.8463012 1.0000000
## L1:d15-L1:d10 1.000000e+00 -2.9296346 4.9296346 0.9980013
## Lx:d15-L1:d10 1.000000e+00 -2.9296346 4.9296346 0.9980013
## L1:d15-Lx:d10 1.083333e+00 -2.8463012 5.0129679 0.9963173
## Lx:d15-Lx:d10 1.083333e+00 -2.8463012 5.0129679 0.9963173
## Lx:d15-L1:d15 1.776357e-15 -3.9296346 3.9296346 1.0000000aov_Larval_stage_appl_F <- aov(F ~ Larval_stage * Bti_application, data = data[Day == 17])
summary(aov_Larval_stage_appl_F)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 86.6 86.63 7.945 0.005905 **
## Bti_application 4 286.0 71.49 6.556 0.000109 ***
## Larval_stage:Bti_application 4 127.6 31.91 2.926 0.025085 *
## Residuals 92 1003.2 10.90
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_appl_F)
TukeyHSD(aov_Larval_stage_appl_F)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = F ~ Larval_stage * Bti_application, data = data[Day == 17])
##
## $Larval_stage
## diff lwr upr p adj
## Lx-L1 1.843137 0.5444019 3.141873 0.005905
##
## $Bti_application
## diff lwr upr p adj
## d0-none -3.2916667 -7.4856401 0.9023068 0.1950195
## d05-none -0.1666667 -4.3606401 4.0273068 0.9999656
## d10-none 0.4583333 -3.7356401 4.6523068 0.9981073
## d15-none 1.2500000 -2.9439735 5.4439735 0.9208637
## d05-d0 3.1250000 0.4724983 5.7775017 0.0125281
## d10-d0 3.7500000 1.0974983 6.4025017 0.0014915
## d15-d0 4.5416667 1.8891649 7.1941684 0.0000681
## d10-d05 0.6250000 -2.0275017 3.2775017 0.9651635
## d15-d05 1.4166667 -1.2358351 4.0691684 0.5740292
## d15-d10 0.7916667 -1.8608351 3.4441684 0.9204931
##
## $`Larval_stage:Bti_application`
## diff lwr upr p adj
## Lx:none-L1:none -3.019807e-14 -8.742740 8.742740 1.0000000
## L1:d0-L1:none -6.166667e+00 -13.078409 0.745076 0.1225393
## Lx:d0-L1:none -4.166667e-01 -7.328409 6.495076 1.0000000
## L1:d05-L1:none -3.333333e-01 -7.245076 6.578409 1.0000000
## Lx:d05-L1:none -1.598721e-14 -6.911743 6.911743 1.0000000
## L1:d10-L1:none -3.333333e-01 -7.245076 6.578409 1.0000000
## Lx:d10-L1:none 1.250000e+00 -5.661743 8.161743 0.9998730
## L1:d15-L1:none 1.166667e+00 -5.745076 8.078409 0.9999288
## Lx:d15-L1:none 1.333333e+00 -5.578409 8.245076 0.9997830
## L1:d0-Lx:none -6.166667e+00 -13.078409 0.745076 0.1225393
## Lx:d0-Lx:none -4.166667e-01 -7.328409 6.495076 1.0000000
## L1:d05-Lx:none -3.333333e-01 -7.245076 6.578409 1.0000000
## Lx:d05-Lx:none 1.421085e-14 -6.911743 6.911743 1.0000000
## L1:d10-Lx:none -3.333333e-01 -7.245076 6.578409 1.0000000
## Lx:d10-Lx:none 1.250000e+00 -5.661743 8.161743 0.9998730
## L1:d15-Lx:none 1.166667e+00 -5.745076 8.078409 0.9999288
## Lx:d15-Lx:none 1.333333e+00 -5.578409 8.245076 0.9997830
## Lx:d0-L1:d0 5.750000e+00 1.378630 10.121370 0.0018908
## L1:d05-L1:d0 5.833333e+00 1.461963 10.204703 0.0015124
## Lx:d05-L1:d0 6.166667e+00 1.795297 10.538037 0.0006044
## L1:d10-L1:d0 5.833333e+00 1.461963 10.204703 0.0015124
## Lx:d10-L1:d0 7.416667e+00 3.045297 11.788037 0.0000146
## L1:d15-L1:d0 7.333333e+00 2.961963 11.704703 0.0000189
## Lx:d15-L1:d0 7.500000e+00 3.128630 11.871370 0.0000112
## L1:d05-Lx:d0 8.333333e-02 -4.288037 4.454703 1.0000000
## Lx:d05-Lx:d0 4.166667e-01 -3.954703 4.788037 0.9999995
## L1:d10-Lx:d0 8.333333e-02 -4.288037 4.454703 1.0000000
## Lx:d10-Lx:d0 1.666667e+00 -2.704703 6.038037 0.9643748
## L1:d15-Lx:d0 1.583333e+00 -2.788037 5.954703 0.9744511
## Lx:d15-Lx:d0 1.750000e+00 -2.621370 6.121370 0.9516636
## Lx:d05-L1:d05 3.333333e-01 -4.038037 4.704703 0.9999999
## L1:d10-L1:d05 0.000000e+00 -4.371370 4.371370 1.0000000
## Lx:d10-L1:d05 1.583333e+00 -2.788037 5.954703 0.9744511
## L1:d15-L1:d05 1.500000e+00 -2.871370 5.871370 0.9822189
## Lx:d15-L1:d05 1.666667e+00 -2.704703 6.038037 0.9643748
## L1:d10-Lx:d05 -3.333333e-01 -4.704703 4.038037 0.9999999
## Lx:d10-Lx:d05 1.250000e+00 -3.121370 5.621370 0.9951586
## L1:d15-Lx:d05 1.166667e+00 -3.204703 5.538037 0.9971192
## Lx:d15-Lx:d05 1.333333e+00 -3.038037 5.704703 0.9922293
## Lx:d10-L1:d10 1.583333e+00 -2.788037 5.954703 0.9744511
## L1:d15-L1:d10 1.500000e+00 -2.871370 5.871370 0.9822189
## Lx:d15-L1:d10 1.666667e+00 -2.704703 6.038037 0.9643748
## L1:d15-Lx:d10 -8.333333e-02 -4.454703 4.288037 1.0000000
## Lx:d15-Lx:d10 8.333333e-02 -4.288037 4.454703 1.0000000
## Lx:d15-L1:d15 1.666667e-01 -4.204703 4.538037 1.0000000#Larval_stage x Treatment
aov_Larval_stage_Treatment <- aov(Percent_Ind ~ Larval_stage * Treatment, data = data[Day == 17])
summary(aov_Larval_stage_Treatment)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 0.365 0.3648 4.664 0.0334 *
## Treatment 4 1.079 0.2696 3.448 0.0113 *
## Larval_stage:Treatment 4 0.154 0.0384 0.491 0.7420
## Residuals 92 7.195 0.0782
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_Treatment)
aov_Larval_stage_Treatment_M <- aov(Percent_Ind_M ~ Larval_stage * Treatment, data = data[Day == 17])
summary(aov_Larval_stage_Treatment_M)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 0.0192 0.01922 0.693 0.4071
## Treatment 4 0.3359 0.08399 3.031 0.0214 *
## Larval_stage:Treatment 4 0.0639 0.01598 0.577 0.6803
## Residuals 92 2.5492 0.02771
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_Treatment_M)
aov_Larval_stage_Treatment_F <- aov(Percent_Ind_F ~ Larval_stage * Treatment, data = data[Day == 17])
summary(aov_Larval_stage_Treatment_F)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 0.2166 0.21657 6.432 0.0129 *
## Treatment 4 0.4099 0.10247 3.044 0.0210 *
## Larval_stage:Treatment 4 0.0345 0.00862 0.256 0.9053
## Residuals 92 3.0975 0.03367
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_Treatment_F)
#Larval_stage x Treatment x Bti_application
aov_Larval_stage_Treatment_Bti_application <- aov(Percent_Ind ~ Larval_stage * Treatment * Bti_application, data = data[Day == 17])
summary(aov_Larval_stage_Treatment_Bti_application)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 0.3648 0.3648 9.137 0.003530 **
## Treatment 4 1.0786 0.2696 6.754 0.000123 ***
## Bti_application 3 3.0161 1.0054 25.181 4.54e-11 ***
## Larval_stage:Treatment 4 0.1537 0.0384 0.963 0.433766
## Larval_stage:Bti_application 3 0.6845 0.2282 5.714 0.001506 **
## Treatment:Bti_application 9 0.6386 0.0710 1.777 0.088700 .
## Larval_stage:Treatment:Bti_application 9 0.1411 0.0157 0.393 0.934561
## Residuals 68 2.7150 0.0399
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_Treatment_Bti_application)
aov_Larval_stage_Treatment_Bti_application_M <- aov(Percent_Ind_M ~ Larval_stage * Treatment * Bti_application, data = data[Day == 17])
summary(aov_Larval_stage_Treatment_Bti_application_M)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 0.0192 0.01922 1.126 0.29229
## Treatment 4 0.3359 0.08399 4.923 0.00152 **
## Bti_application 3 0.7977 0.26590 15.587 8.11e-08 ***
## Larval_stage:Treatment 4 0.0639 0.01598 0.937 0.44815
## Larval_stage:Bti_application 3 0.0815 0.02715 1.592 0.19942
## Treatment:Bti_application 9 0.4046 0.04495 2.635 0.01117 *
## Larval_stage:Treatment:Bti_application 9 0.1054 0.01171 0.687 0.71842
## Residuals 68 1.1600 0.01706
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_Treatment_Bti_application_M)
aov_Larval_stage_Treatment_Bti_application_F <- aov(Percent_Ind_F ~ Larval_stage * Treatment * Bti_application, data = data[Day == 17])
summary(aov_Larval_stage_Treatment_Bti_application_F)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 0.2166 0.21657 8.062 0.00596 **
## Treatment 4 0.4099 0.10247 3.815 0.00741 **
## Bti_application 3 0.7122 0.23740 8.837 5.02e-05 ***
## Larval_stage:Treatment 4 0.0345 0.00862 0.321 0.86309
## Larval_stage:Bti_application 3 0.3055 0.10184 3.791 0.01415 *
## Treatment:Bti_application 9 0.1964 0.02182 0.812 0.60670
## Larval_stage:Treatment:Bti_application 9 0.0568 0.00631 0.235 0.98816
## Residuals 68 1.8267 0.02686
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_Treatment_Bti_application_F)
3.2 Duration of emergence
# Treatment
aov_Treatment_Em <- aov(Emergence_all ~ Treatment, data = data[Day == 17 & Ind > 0])
summary(aov_Treatment_Em)## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 4 30.7 7.675 1.021 0.401
## Residuals 94 706.9 7.521plot(aov_Treatment_Em)
aov_Treatment_EmM <- aov(Emergenz_M ~ Treatment, data = data[Day == 17 & M > 0])
summary(aov_Treatment_EmM)## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 4 17.6 4.388 1.021 0.401
## Residuals 92 395.5 4.298plot(aov_Treatment_EmM) 
aov_Treatment_EmF <- aov(Emergenz_F ~ Treatment, data = data[Day == 17 & F > 0])
summary(aov_Treatment_EmF)## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 4 35.6 8.900 1.393 0.242
## Residuals 93 594.1 6.388plot(aov_Treatment_EmF)
#Time (Bti_application)
aov_Bti_application_Em <- aov(Emergence_all ~ Bti_application, data = data[Day == 17 & Ind > 0])
summary(aov_Bti_application_Em)## Df Sum Sq Mean Sq F value Pr(>F)
## Bti_application 4 104.3 26.068 3.869 0.00592 **
## Residuals 94 633.4 6.738
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Bti_application_Em)
TukeyHSD(aov_Bti_application_Em)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Emergence_all ~ Bti_application, data = data[Day == 17 & Ind > 0])
##
## $Bti_application
## diff lwr upr p adj
## d0-none -1.64285714 -4.98502476 1.699310 0.6499779
## d05-none 0.04166667 -3.25375505 3.337088 0.9999996
## d10-none 0.50000000 -2.79542171 3.795422 0.9932655
## d15-none 1.33333333 -1.96208838 4.628755 0.7927545
## d05-d0 1.68452381 -0.47283611 3.841884 0.1994975
## d10-d0 2.14285714 -0.01450278 4.300217 0.0524579
## d15-d0 2.97619048 0.81883056 5.133550 0.0020587
## d10-d05 0.45833333 -1.62587436 2.542541 0.9729225
## d15-d05 1.29166667 -0.79254103 3.375874 0.4245593
## d15-d10 0.83333333 -1.25087436 2.917541 0.7997881aov_Bti_application_EmM <- aov(Emergenz_M ~ Bti_application, data = data[Day == 17 & M > 0])
summary(aov_Bti_application_EmM)## Df Sum Sq Mean Sq F value Pr(>F)
## Bti_application 4 39.1 9.787 2.408 0.0549 .
## Residuals 92 373.9 4.064
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Bti_application_EmM) 
TukeyHSD(aov_Bti_application_EmM)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Emergenz_M ~ Bti_application, data = data[Day == 17 & M > 0])
##
## $Bti_application
## diff lwr upr p adj
## d0-none -0.9473684 -3.57421176 1.679475 0.8531119
## d05-none 0.2083333 -2.35199685 2.768664 0.9994066
## d10-none 0.6250000 -1.93533018 3.185330 0.9604571
## d15-none 0.8333333 -1.72699685 3.393664 0.8940389
## d05-d0 1.1557018 -0.56683462 2.878238 0.3424397
## d10-d0 1.5723684 -0.15016795 3.294905 0.0907048
## d15-d0 1.7807018 0.05816538 3.503238 0.0391064
## d10-d05 0.4166667 -1.20262832 2.035962 0.9523348
## d15-d05 0.6250000 -0.99429499 2.244295 0.8193779
## d15-d10 0.2083333 -1.41096166 1.827628 0.9964263aov_Bti_application_EmF <- aov(Emergenz_F ~ Bti_application, data = data[Day == 17 & F > 0])
summary(aov_Bti_application_EmF)## Df Sum Sq Mean Sq F value Pr(>F)
## Bti_application 4 60.2 15.041 2.456 0.051 .
## Residuals 93 569.5 6.124
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Bti_application_EmF)
TukeyHSD(aov_Bti_application_EmF)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Emergenz_F ~ Bti_application, data = data[Day == 17 & F > 0])
##
## $Bti_application
## diff lwr upr p adj
## d0-none -1.6666667 -4.8535479 1.520215 0.5940692
## d05-none -0.3478261 -3.5037660 2.808114 0.9980456
## d10-none -0.5000000 -3.6423072 2.642307 0.9919149
## d15-none 0.6250000 -2.5173072 3.767307 0.9812700
## d05-d0 1.3188406 -0.7590470 3.396728 0.3996671
## d10-d0 1.1666667 -0.8904563 3.223790 0.5152979
## d15-d0 2.2916667 0.2345437 4.348790 0.0211152
## d10-d05 -0.1521739 -2.1610292 1.856681 0.9995533
## d15-d05 0.9728261 -1.0360292 2.981681 0.6625555
## d15-d10 1.1250000 -0.8623696 3.112370 0.5171756#Larval_stage
aov_Larval_stage_Em <- aov(Emergence_all ~ Larval_stage, data = data[Day == 17 & Ind > 0])
summary(aov_Larval_stage_Em)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 109.7 109.74 16.95 8.06e-05 ***
## Residuals 97 627.9 6.47
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_Em)
TukeyHSD(aov_Larval_stage_Em)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Emergence_all ~ Larval_stage, data = data[Day == 17 & Ind > 0])
##
## $Larval_stage
## diff lwr upr p adj
## Lx-L1 2.106618 1.091137 3.122098 8.06e-05aov_Larval_stage_EmM <- aov(Emergenz_M ~ Larval_stage, data = data[Day == 17 & M > 0])
summary(aov_Larval_stage_EmM)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 7.5 7.461 1.748 0.189
## Residuals 95 405.5 4.269plot(aov_Larval_stage_EmM) 
TukeyHSD(aov_Larval_stage_EmM )## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Emergenz_M ~ Larval_stage, data = data[Day == 17 & M > 0])
##
## $Larval_stage
## diff lwr upr p adj
## Lx-L1 0.5554135 -0.2786467 1.389474 0.1893395aov_Larval_stage_EmF <- aov(Emergenz_F ~ Larval_stage, data = data[Day == 17 & F > 0])
summary(aov_Larval_stage_EmF)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 118.4 118.41 22.23 8.18e-06 ***
## Residuals 96 511.3 5.33
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_EmF)
TukeyHSD(aov_Larval_stage_EmF)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Emergenz_F ~ Larval_stage, data = data[Day == 17 & F > 0])
##
## $Larval_stage
## diff lwr upr p adj
## Lx-L1 2.20025 1.274011 3.126489 8.2e-06#Treatment x Bti_application
aov_Treatment_appl_Em <- aov(Emergence_all ~ Treatment * Bti_application, data = data[Day == 17 & Ind > 0])
summary(aov_Treatment_appl_Em)## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 4 30.7 7.68 1.136 0.34553
## Bti_application 3 98.8 32.95 4.875 0.00361 **
## Treatment:Bti_application 9 53.9 5.99 0.887 0.54083
## Residuals 82 554.2 6.76
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Treatment_appl_Em)
aov_Treatment_appl_EmM <- aov(Emergenz_M ~ Treatment * Bti_application, data = data[Day == 17 & M > 0])
summary(aov_Treatment_appl_EmM)## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 4 17.6 4.388 1.091 0.3666
## Bti_application 3 39.7 13.243 3.293 0.0247 *
## Treatment:Bti_application 9 34.1 3.785 0.941 0.4946
## Residuals 80 321.7 4.021
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Treatment_appl_EmM)
aov_Treatment_appl_EmF <- aov(Emergenz_F ~ Treatment * Bti_application, data = data[Day == 17 & F > 0])
summary(aov_Treatment_appl_EmF)## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 4 35.6 8.900 1.410 0.2382
## Bti_application 3 53.6 17.868 2.830 0.0435 *
## Treatment:Bti_application 9 29.1 3.234 0.512 0.8617
## Residuals 81 511.4 6.313
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Treatment_appl_EmF)
#Larval_stage x Bti_application
aov_Larval_stage_appl_Em <- aov(Emergence_all ~ Larval_stage * Bti_application, data = data[Day == 17 & Ind > 0])
summary(aov_Larval_stage_appl_Em)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 109.7 109.74 20.114 2.17e-05 ***
## Bti_application 4 116.0 29.00 5.316 0.000693 ***
## Larval_stage:Bti_application 4 26.3 6.58 1.206 0.313766
## Residuals 89 485.6 5.46
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_appl_Em) 
aov_Larval_stage_appl_EmM <- aov(Emergenz_M ~ Larval_stage * Bti_application, data = data[Day == 17 & M > 0])
summary(aov_Larval_stage_appl_EmM)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 7.5 7.461 1.829 0.1798
## Bti_application 4 42.9 10.721 2.628 0.0398 *
## Larval_stage:Bti_application 4 7.7 1.934 0.474 0.7547
## Residuals 87 354.9 4.080
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_appl_EmM) 
aov_Larval_stage_appl_EmF <- aov(Emergenz_F ~ Larval_stage * Bti_application, data = data[Day == 17 & F > 0])
summary(aov_Larval_stage_appl_EmF)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 118.4 118.41 25.443 2.43e-06 ***
## Bti_application 4 68.9 17.22 3.700 0.00785 **
## Larval_stage:Bti_application 4 32.8 8.21 1.764 0.14328
## Residuals 88 409.5 4.65
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_appl_EmF)
#Larval_stage x Treatment
aov_Larval_stage_Treatment_Em <- aov(Emergence_all ~ Larval_stage * Treatment, data = data[Day == 17 & Ind > 0])
summary(aov_Larval_stage_Treatment_Em)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 109.7 109.74 17.016 8.32e-05 ***
## Treatment 4 27.1 6.78 1.051 0.386
## Larval_stage:Treatment 4 26.8 6.71 1.040 0.391
## Residuals 89 574.0 6.45
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_Treatment_Em)
aov_Larval_stage_Treatment_EmM <- aov(Emergenz_M ~ Larval_stage * Treatment, data = data[Day == 17 & M > 0])
summary(aov_Larval_stage_Treatment_EmM)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 7.5 7.461 1.828 0.1798
## Treatment 4 17.7 4.414 1.082 0.3706
## Larval_stage:Treatment 4 32.9 8.224 2.015 0.0993 .
## Residuals 87 355.0 4.080
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_Treatment_EmM) 
aov_Larval_stage_Treatment_EmF <- aov(Emergenz_F ~ Larval_stage * Treatment, data = data[Day == 17 & F > 0])
summary(aov_Larval_stage_Treatment_EmF)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 118.4 118.41 22.241 8.97e-06 ***
## Treatment 4 28.9 7.22 1.357 0.255
## Larval_stage:Treatment 4 13.9 3.47 0.651 0.627
## Residuals 88 468.5 5.32
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_Treatment_EmF)
#Larval_stage x Treatment x Bti_application
aov_Larval_stage_Treatment_Bti_application_Em <- aov(Emergence_all ~ Larval_stage * Treatment * Bti_application, data = data[Day == 17 & Ind > 0])
summary(aov_Larval_stage_Treatment_Bti_application_Em)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 109.7 109.74 19.861 3.3e-05 ***
## Treatment 4 27.1 6.78 1.226 0.3081
## Bti_application 3 111.4 37.15 6.723 0.0005 ***
## Larval_stage:Treatment 4 17.1 4.28 0.774 0.5458
## Larval_stage:Bti_application 3 18.5 6.17 1.116 0.3489
## Treatment:Bti_application 9 61.8 6.86 1.242 0.2855
## Larval_stage:Treatment:Bti_application 8 27.3 3.41 0.618 0.7597
## Residuals 66 364.7 5.53
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_Treatment_Bti_application_Em)
aov_Larval_stage_Treatment_Bti_application_EmM <- aov(Emergenz_M ~ Larval_stage * Treatment * Bti_application, data = data[Day == 17 & M > 0])
summary(aov_Larval_stage_Treatment_Bti_application_EmM)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 7.46 7.461 1.773 0.1877
## Treatment 4 17.65 4.414 1.049 0.3892
## Bti_application 3 43.92 14.639 3.479 0.0209 *
## Larval_stage:Treatment 4 27.45 6.863 1.631 0.1774
## Larval_stage:Bti_application 3 0.66 0.219 0.052 0.9842
## Treatment:Bti_application 9 31.60 3.511 0.834 0.5873
## Larval_stage:Treatment:Bti_application 8 14.94 1.867 0.444 0.8902
## Residuals 64 269.33 4.208
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_Treatment_Bti_application_EmM) ## Warning: not plotting observations with leverage one:
## 19
aov_Larval_stage_Treatment_Bti_application_EmF <- aov(Emergenz_F ~ Larval_stage * Treatment * Bti_application, data = data[Day == 17 & F > 0])
summary(aov_Larval_stage_Treatment_Bti_application_EmF)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 118.41 118.41 28.790 1.15e-06 ***
## Treatment 4 28.89 7.22 1.756 0.14845
## Bti_application 3 62.62 20.87 5.075 0.00322 **
## Larval_stage:Treatment 4 8.71 2.18 0.529 0.71449
## Larval_stage:Bti_application 3 28.71 9.57 2.326 0.08284 .
## Treatment:Bti_application 9 34.32 3.81 0.927 0.50770
## Larval_stage:Treatment:Bti_application 8 80.68 10.08 2.452 0.02193 *
## Residuals 65 267.33 4.11
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_Treatment_Bti_application_EmF)
TukeyHSD(aov_Larval_stage_Treatment_Bti_application_EmF)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Emergenz_F ~ Larval_stage * Treatment * Bti_application, data = data[Day == 17 & F > 0])
##
## $Larval_stage
## diff lwr upr p adj
## Lx-L1 2.20025 1.381301 3.019199 1.2e-06
##
## $Treatment
## diff lwr upr p adj
## 05%-Ctrl -0.08333333 -2.680559 2.5138924 0.9999847
## 10%-Ctrl -0.79166667 -3.388892 1.8055591 0.9119579
## 25%-Ctrl -1.16666667 -3.763892 1.4305591 0.7160641
## 50%-Ctrl 0.22997497 -2.418686 2.8786359 0.9992011
## 10%-05% -0.70833333 -2.350963 0.9342965 0.7456548
## 25%-05% -1.08333333 -2.725963 0.5592965 0.3542697
## 50%-05% 0.31330830 -1.409496 2.0361130 0.9860620
## 25%-10% -0.37500000 -2.017630 1.2676298 0.9677912
## 50%-10% 1.02164164 -0.701163 2.7444463 0.4631356
## 50%-25% 1.39664164 -0.326163 3.1194463 0.1664049
##
## $Bti_application
## diff lwr upr p adj
## d0-none -1.27013521 -3.9042029 1.363932 0.6594436
## d05-none 0.08695652 -2.5215371 2.695450 0.9999822
## d10-none -0.04848251 -2.6457083 2.548743 0.9999982
## d15-none 1.07651749 -1.5207083 3.673743 0.7721652
## d05-d0 1.35709174 -0.3603543 3.074538 0.1864612
## d10-d0 1.22165270 -0.4786307 2.921936 0.2700656
## d15-d0 2.34665270 0.6463693 4.046936 0.0022787
## d10-d05 -0.13543903 -1.7958275 1.524949 0.9993756
## d15-d05 0.98956097 -0.6708275 2.649949 0.4580278
## d15-d10 1.12500000 -0.5176298 2.767630 0.3164478
##
## $`Larval_stage:Treatment`
## diff lwr upr p adj
## Lx:Ctrl-L1:Ctrl 3.24390612 -2.18060335 8.66841560 0.6297492
## L1:05%-L1:Ctrl 0.41666667 -3.87178462 4.70511795 0.9999993
## Lx:05%-L1:Ctrl 2.66057279 -1.62787849 6.94902407 0.5801370
## L1:10%-L1:Ctrl -0.50000000 -4.78845128 3.78845128 0.9999966
## Lx:10%-L1:Ctrl 2.16057279 -2.12787849 6.44902407 0.8180377
## L1:25%-L1:Ctrl -0.58333333 -4.87178462 3.70511795 0.9999869
## Lx:25%-L1:Ctrl 1.49390612 -2.79454516 5.78235741 0.9782650
## L1:50%-L1:Ctrl 1.40397423 -3.09379142 5.90173988 0.9897729
## Lx:50%-L1:Ctrl 2.51727228 -1.77117900 6.80572356 0.6538423
## L1:05%-Lx:Ctrl -2.82723946 -7.11569074 1.46121183 0.4940282
## Lx:05%-Lx:Ctrl -0.58333333 -4.87178462 3.70511795 0.9999869
## L1:10%-Lx:Ctrl -3.74390612 -8.03235741 0.54454516 0.1386489
## Lx:10%-Lx:Ctrl -1.08333333 -5.37178462 3.20511795 0.9978630
## L1:25%-Lx:Ctrl -3.82723946 -8.11569074 0.46121183 0.1199694
## Lx:25%-Lx:Ctrl -1.75000000 -6.03845128 2.53845128 0.9410411
## L1:50%-Lx:Ctrl -1.83993189 -6.33769754 2.65783376 0.9401763
## Lx:50%-Lx:Ctrl -0.72663384 -5.01508513 3.56181744 0.9999152
## Lx:05%-L1:05% 2.24390612 -0.46834862 4.95616086 0.1914361
## L1:10%-L1:05% -0.91666667 -3.62892140 1.79558807 0.9822632
## Lx:10%-L1:05% 1.74390612 -0.96834862 4.45616086 0.5299434
## L1:25%-L1:05% -1.00000000 -3.71225474 1.71225474 0.9685020
## Lx:25%-L1:05% 1.07723946 -1.63501528 3.78949419 0.9498785
## L1:50%-L1:05% 0.98730757 -2.04508541 4.01970055 0.9862654
## Lx:50%-L1:05% 2.10060561 -0.61164913 4.81286035 0.2690008
## L1:10%-Lx:05% -3.16057279 -5.87282753 -0.44831805 0.0105583
## Lx:10%-Lx:05% -0.50000000 -3.21225474 2.21225474 0.9998288
## L1:25%-Lx:05% -3.24390612 -5.95616086 -0.53165138 0.0077232
## Lx:25%-Lx:05% -1.16666667 -3.87892140 1.54558807 0.9199964
## L1:50%-Lx:05% -1.25659855 -4.28899154 1.77579443 0.9354542
## Lx:50%-Lx:05% -0.14330051 -2.85555525 2.56895423 1.0000000
## Lx:10%-L1:10% 2.66057279 -0.05168195 5.37282753 0.0588947
## L1:25%-L1:10% -0.08333333 -2.79558807 2.62892140 1.0000000
## Lx:25%-L1:10% 1.99390612 -0.71834862 4.70616086 0.3382055
## L1:50%-L1:10% 1.90397423 -1.12841875 4.93636722 0.5634957
## Lx:50%-L1:10% 3.01727228 0.30501754 5.72952702 0.0177858
## L1:25%-Lx:10% -2.74390612 -5.45616086 -0.03165138 0.0451517
## Lx:25%-Lx:10% -0.66666667 -3.37892140 2.04558807 0.9982701
## L1:50%-Lx:10% -0.75659855 -3.78899154 2.27579443 0.9980576
## Lx:50%-Lx:10% 0.35669949 -2.35555525 3.06895423 0.9999902
## Lx:25%-L1:25% 2.07723946 -0.63501528 4.78949419 0.2833473
## L1:50%-L1:25% 1.98730757 -1.04508541 5.01970055 0.5025995
## Lx:50%-L1:25% 3.10060561 0.38835087 5.81286035 0.0131670
## L1:50%-Lx:25% -0.08993189 -3.12232487 2.94246109 1.0000000
## Lx:50%-Lx:25% 1.02336616 -1.68888858 3.73562089 0.9635156
## Lx:50%-L1:50% 1.11329804 -1.91909494 4.14569103 0.9693494
##
## $`Larval_stage:Bti_application`
## diff lwr upr p adj
## Lx:none-L1:none 2.2896775 -3.1348320 7.7141870 0.9281684
## L1:d0-L1:none -1.8366769 -6.2657704 2.5924165 0.9351869
## Lx:d0-L1:none 1.4332702 -2.8551811 5.7217215 0.9835611
## L1:d05-L1:none 0.9547805 -3.3724811 5.2820421 0.9992607
## Lx:d05-L1:none 1.5774026 -2.7110487 5.8658539 0.9689768
## L1:d10-L1:none -0.5779564 -4.8664077 3.7104949 0.9999879
## Lx:d10-L1:none 2.7706689 -1.5177824 7.0591202 0.5230694
## L1:d15-L1:none 1.2553769 -3.0330743 5.5438282 0.9935500
## Lx:d15-L1:none 3.1873356 -1.1011157 7.4757868 0.3232195
## L1:d0-Lx:none -4.1263544 -8.5554479 0.3027390 0.0884465
## Lx:d0-Lx:none -0.8564073 -5.1448586 3.4320439 0.9996696
## L1:d05-Lx:none -1.3348971 -5.6621586 2.9923645 0.9905943
## Lx:d05-Lx:none -0.7122749 -5.0007262 3.5761764 0.9999283
## L1:d10-Lx:none -2.8676339 -7.1560852 1.4208174 0.4735186
## Lx:d10-Lx:none 0.4809914 -3.8074599 4.7694427 0.9999975
## L1:d15-Lx:none -1.0343006 -5.3227519 3.2541507 0.9985072
## Lx:d15-Lx:none 0.8976580 -3.3907932 5.1861093 0.9995158
## Lx:d0-L1:d0 3.2699471 0.3403772 6.1995170 0.0171554
## L1:d05-L1:d0 2.7914574 -0.1946368 5.7775516 0.0862285
## Lx:d05-L1:d0 3.4140795 0.4845096 6.3436495 0.0105485
## L1:d10-L1:d0 1.2587205 -1.6708494 4.1882905 0.9205063
## Lx:d10-L1:d0 4.6073458 1.6777759 7.5369158 0.0001080
## L1:d15-L1:d0 3.0920539 0.1624839 6.0216238 0.0304490
## Lx:d15-L1:d0 5.0240125 2.0944425 7.9535824 0.0000185
## L1:d05-Lx:d0 -0.4784897 -3.2517016 2.2947222 0.9999013
## Lx:d05-Lx:d0 0.1441324 -2.5681223 2.8563872 1.0000000
## L1:d10-Lx:d0 -2.0112266 -4.7234813 0.7010282 0.3263447
## Lx:d10-Lx:d0 1.3373987 -1.3748560 4.0496535 0.8358293
## L1:d15-Lx:d0 -0.1778932 -2.8901480 2.5343615 1.0000000
## Lx:d15-Lx:d0 1.7540654 -0.9581894 4.4663201 0.5216499
## Lx:d05-L1:d05 0.6226222 -2.1505897 3.3958340 0.9991508
## L1:d10-L1:d05 -1.5327369 -4.3059488 1.2404750 0.7261796
## Lx:d10-L1:d05 1.8158884 -0.9573235 4.5891003 0.5038374
## L1:d15-L1:d05 0.3005965 -2.4726154 3.0738084 0.9999982
## Lx:d15-L1:d05 2.2325551 -0.5406568 5.0057670 0.2219421
## L1:d10-Lx:d05 -2.1553590 -4.8676138 0.5568957 0.2372353
## Lx:d10-Lx:d05 1.1932663 -1.5189885 3.9055210 0.9092472
## L1:d15-Lx:d05 -0.3220257 -3.0342804 2.3902291 0.9999960
## Lx:d15-Lx:d05 1.6099329 -1.1023218 4.3221877 0.6394731
## Lx:d10-L1:d10 3.3486253 0.6363706 6.0608800 0.0051662
## L1:d15-L1:d10 1.8333333 -0.8789214 4.5455881 0.4578830
## Lx:d15-L1:d10 3.7652920 1.0530372 6.4775467 0.0009542
## L1:d15-Lx:d10 -1.5152920 -4.2275467 1.1969628 0.7139133
## Lx:d15-Lx:d10 0.4166667 -2.2955881 3.1289214 0.9999629
## Lx:d15-L1:d15 1.9319586 -0.7802961 4.6442134 0.3824434
##
## $`Treatment:Bti_application`
## diff lwr upr p adj
## 05%:none-Ctrl:none NA NA NA NA
## 10%:none-Ctrl:none NA NA NA NA
## 25%:none-Ctrl:none NA NA NA NA
## 50%:none-Ctrl:none NA NA NA NA
## Ctrl:d0-Ctrl:none NA NA NA NA
## 05%:d0-Ctrl:none -1.521531e-01 -4.62516972 4.320864 1.0000000
## 10%:d0-Ctrl:none -2.152153e+00 -6.62516972 2.320864 0.9756561
## 25%:d0-Ctrl:none -2.985486e+00 -7.45850306 1.487530 0.6547972
## 50%:d0-Ctrl:none -2.187207e+00 -7.66551069 3.291098 0.9976182
## Ctrl:d05-Ctrl:none NA NA NA NA
## 05%:d05-Ctrl:none -2.107131e-01 -4.68372972 4.262304 1.0000000
## 10%:d05-Ctrl:none -5.440464e-01 -5.01706305 3.928970 1.0000000
## 25%:d05-Ctrl:none -1.044046e+00 -5.51706305 3.428970 0.9999998
## 50%:d05-Ctrl:none 3.385421e-01 -4.35279727 5.029882 1.0000000
## Ctrl:d10-Ctrl:none NA NA NA NA
## 05%:d10-Ctrl:none -1.519002e-01 -4.62491685 4.321116 1.0000000
## 10%:d10-Ctrl:none -3.185669e-01 -4.79158352 4.154450 1.0000000
## 25%:d10-Ctrl:none -8.185669e-01 -5.29158352 3.654450 1.0000000
## 50%:d10-Ctrl:none -7.109660e-01 -5.18398259 3.762051 1.0000000
## Ctrl:d15-Ctrl:none NA NA NA NA
## 05%:d15-Ctrl:none 1.814331e-01 -4.29158352 4.654450 1.0000000
## 10%:d15-Ctrl:none -1.519002e-01 -4.62491685 4.321116 1.0000000
## 25%:d15-Ctrl:none 1.814331e-01 -4.29158352 4.654450 1.0000000
## 50%:d15-Ctrl:none 2.289034e+00 -2.18398259 6.762051 0.9537198
## 10%:none-05%:none NA NA NA NA
## 25%:none-05%:none NA NA NA NA
## 50%:none-05%:none NA NA NA NA
## Ctrl:d0-05%:none NA NA NA NA
## 05%:d0-05%:none NA NA NA NA
## 10%:d0-05%:none NA NA NA NA
## 25%:d0-05%:none NA NA NA NA
## 50%:d0-05%:none NA NA NA NA
## Ctrl:d05-05%:none NA NA NA NA
## 05%:d05-05%:none NA NA NA NA
## 10%:d05-05%:none NA NA NA NA
## 25%:d05-05%:none NA NA NA NA
## 50%:d05-05%:none NA NA NA NA
## Ctrl:d10-05%:none NA NA NA NA
## 05%:d10-05%:none NA NA NA NA
## 10%:d10-05%:none NA NA NA NA
## 25%:d10-05%:none NA NA NA NA
## 50%:d10-05%:none NA NA NA NA
## Ctrl:d15-05%:none NA NA NA NA
## 05%:d15-05%:none NA NA NA NA
## 10%:d15-05%:none NA NA NA NA
## 25%:d15-05%:none NA NA NA NA
## 50%:d15-05%:none NA NA NA NA
## 25%:none-10%:none NA NA NA NA
## 50%:none-10%:none NA NA NA NA
## Ctrl:d0-10%:none NA NA NA NA
## 05%:d0-10%:none NA NA NA NA
## 10%:d0-10%:none NA NA NA NA
## 25%:d0-10%:none NA NA NA NA
## 50%:d0-10%:none NA NA NA NA
## Ctrl:d05-10%:none NA NA NA NA
## 05%:d05-10%:none NA NA NA NA
## 10%:d05-10%:none NA NA NA NA
## 25%:d05-10%:none NA NA NA NA
## 50%:d05-10%:none NA NA NA NA
## Ctrl:d10-10%:none NA NA NA NA
## 05%:d10-10%:none NA NA NA NA
## 10%:d10-10%:none NA NA NA NA
## 25%:d10-10%:none NA NA NA NA
## 50%:d10-10%:none NA NA NA NA
## Ctrl:d15-10%:none NA NA NA NA
## 05%:d15-10%:none NA NA NA NA
## 10%:d15-10%:none NA NA NA NA
## 25%:d15-10%:none NA NA NA NA
## 50%:d15-10%:none NA NA NA NA
## 50%:none-25%:none NA NA NA NA
## Ctrl:d0-25%:none NA NA NA NA
## 05%:d0-25%:none NA NA NA NA
## 10%:d0-25%:none NA NA NA NA
## 25%:d0-25%:none NA NA NA NA
## 50%:d0-25%:none NA NA NA NA
## Ctrl:d05-25%:none NA NA NA NA
## 05%:d05-25%:none NA NA NA NA
## 10%:d05-25%:none NA NA NA NA
## 25%:d05-25%:none NA NA NA NA
## 50%:d05-25%:none NA NA NA NA
## Ctrl:d10-25%:none NA NA NA NA
## 05%:d10-25%:none NA NA NA NA
## 10%:d10-25%:none NA NA NA NA
## 25%:d10-25%:none NA NA NA NA
## 50%:d10-25%:none NA NA NA NA
## Ctrl:d15-25%:none NA NA NA NA
## 05%:d15-25%:none NA NA NA NA
## 10%:d15-25%:none NA NA NA NA
## 25%:d15-25%:none NA NA NA NA
## 50%:d15-25%:none NA NA NA NA
## Ctrl:d0-50%:none NA NA NA NA
## 05%:d0-50%:none NA NA NA NA
## 10%:d0-50%:none NA NA NA NA
## 25%:d0-50%:none NA NA NA NA
## 50%:d0-50%:none NA NA NA NA
## Ctrl:d05-50%:none NA NA NA NA
## 05%:d05-50%:none NA NA NA NA
## 10%:d05-50%:none NA NA NA NA
## 25%:d05-50%:none NA NA NA NA
## 50%:d05-50%:none NA NA NA NA
## Ctrl:d10-50%:none NA NA NA NA
## 05%:d10-50%:none NA NA NA NA
## 10%:d10-50%:none NA NA NA NA
## 25%:d10-50%:none NA NA NA NA
## 50%:d10-50%:none NA NA NA NA
## Ctrl:d15-50%:none NA NA NA NA
## 05%:d15-50%:none NA NA NA NA
## 10%:d15-50%:none NA NA NA NA
## 25%:d15-50%:none NA NA NA NA
## 50%:d15-50%:none NA NA NA NA
## 05%:d0-Ctrl:d0 NA NA NA NA
## 10%:d0-Ctrl:d0 NA NA NA NA
## 25%:d0-Ctrl:d0 NA NA NA NA
## 50%:d0-Ctrl:d0 NA NA NA NA
## Ctrl:d05-Ctrl:d0 NA NA NA NA
## 05%:d05-Ctrl:d0 NA NA NA NA
## 10%:d05-Ctrl:d0 NA NA NA NA
## 25%:d05-Ctrl:d0 NA NA NA NA
## 50%:d05-Ctrl:d0 NA NA NA NA
## Ctrl:d10-Ctrl:d0 NA NA NA NA
## 05%:d10-Ctrl:d0 NA NA NA NA
## 10%:d10-Ctrl:d0 NA NA NA NA
## 25%:d10-Ctrl:d0 NA NA NA NA
## 50%:d10-Ctrl:d0 NA NA NA NA
## Ctrl:d15-Ctrl:d0 NA NA NA NA
## 05%:d15-Ctrl:d0 NA NA NA NA
## 10%:d15-Ctrl:d0 NA NA NA NA
## 25%:d15-Ctrl:d0 NA NA NA NA
## 50%:d15-Ctrl:d0 NA NA NA NA
## 10%:d0-05%:d0 -2.000000e+00 -6.47301662 2.473017 0.9895651
## 25%:d0-05%:d0 -2.833333e+00 -7.30634995 1.639683 0.7438927
## 50%:d0-05%:d0 -2.035053e+00 -7.51335759 3.443251 0.9991584
## Ctrl:d05-05%:d0 NA NA NA NA
## 05%:d05-05%:d0 -5.856000e-02 -4.53157662 4.414457 1.0000000
## 10%:d05-05%:d0 -3.918933e-01 -4.86490995 4.081123 1.0000000
## 25%:d05-05%:d0 -8.918933e-01 -5.36490995 3.581123 1.0000000
## 50%:d05-05%:d0 4.906952e-01 -4.20064417 5.182035 1.0000000
## Ctrl:d10-05%:d0 NA NA NA NA
## 05%:d10-05%:d0 2.528737e-04 -4.47276374 4.473269 1.0000000
## 10%:d10-05%:d0 -1.664138e-01 -4.63943041 4.306603 1.0000000
## 25%:d10-05%:d0 -6.664138e-01 -5.13943041 3.806603 1.0000000
## 50%:d10-05%:d0 -5.588129e-01 -5.03182949 3.914204 1.0000000
## Ctrl:d15-05%:d0 NA NA NA NA
## 05%:d15-05%:d0 3.335862e-01 -4.13943041 4.806603 1.0000000
## 10%:d15-05%:d0 2.528737e-04 -4.47276374 4.473269 1.0000000
## 25%:d15-05%:d0 3.335862e-01 -4.13943041 4.806603 1.0000000
## 50%:d15-05%:d0 2.441187e+00 -2.03182949 6.914204 0.9156427
## 25%:d0-10%:d0 -8.333333e-01 -5.30634995 3.639683 1.0000000
## 50%:d0-10%:d0 -3.505343e-02 -5.51335759 5.443251 1.0000000
## Ctrl:d05-10%:d0 NA NA NA NA
## 05%:d05-10%:d0 1.941440e+00 -2.53157662 6.414457 0.9927799
## 10%:d05-10%:d0 1.608107e+00 -2.86490995 6.081123 0.9994876
## 25%:d05-10%:d0 1.108107e+00 -3.36490995 5.581123 0.9999992
## 50%:d05-10%:d0 2.490695e+00 -2.20064417 7.182035 0.9341974
## Ctrl:d10-10%:d0 NA NA NA NA
## 05%:d10-10%:d0 2.000253e+00 -2.47276374 6.473269 0.9895491
## 10%:d10-10%:d0 1.833586e+00 -2.63943041 6.306603 0.9965802
## 25%:d10-10%:d0 1.333586e+00 -3.13943041 5.806603 0.9999768
## 50%:d10-10%:d0 1.441187e+00 -3.03182949 5.914204 0.9999120
## Ctrl:d15-10%:d0 NA NA NA NA
## 05%:d15-10%:d0 2.333586e+00 -2.13943041 6.806603 0.9441807
## 10%:d15-10%:d0 2.000253e+00 -2.47276374 6.473269 0.9895491
## 25%:d15-10%:d0 2.333586e+00 -2.13943041 6.806603 0.9441807
## 50%:d15-10%:d0 4.441187e+00 -0.03182949 8.914204 0.0539226
## 50%:d0-25%:d0 7.982799e-01 -4.68002426 6.276584 1.0000000
## Ctrl:d05-25%:d0 NA NA NA NA
## 05%:d05-25%:d0 2.774773e+00 -1.69824328 7.247790 0.7756246
## 10%:d05-25%:d0 2.441440e+00 -2.03157662 6.914457 0.9155661
## 25%:d05-25%:d0 1.941440e+00 -2.53157662 6.414457 0.9927799
## 50%:d05-25%:d0 3.324029e+00 -1.36731083 8.015368 0.5407649
## Ctrl:d10-25%:d0 NA NA NA NA
## 05%:d10-25%:d0 2.833586e+00 -1.63943041 7.306603 0.7437521
## 10%:d10-25%:d0 2.666920e+00 -1.80609708 7.139936 0.8290540
## 25%:d10-25%:d0 2.166920e+00 -2.30609708 6.639936 0.9737749
## 50%:d10-25%:d0 2.274520e+00 -2.19849616 6.747537 0.9565565
## Ctrl:d15-25%:d0 NA NA NA NA
## 05%:d15-25%:d0 3.166920e+00 -1.30609708 7.639936 0.5422699
## 10%:d15-25%:d0 2.833586e+00 -1.63943041 7.306603 0.7437521
## 25%:d15-25%:d0 3.166920e+00 -1.30609708 7.639936 0.5422699
## 50%:d15-25%:d0 5.274520e+00 0.80150384 9.747537 0.0061204
## Ctrl:d05-50%:d0 NA NA NA NA
## 05%:d05-50%:d0 1.976493e+00 -3.50181073 7.454798 0.9994590
## 10%:d05-50%:d0 1.643160e+00 -3.83514407 7.121464 0.9999742
## 25%:d05-50%:d0 1.143160e+00 -4.33514407 6.621464 1.0000000
## 50%:d05-50%:d0 2.525749e+00 -3.13221954 8.183717 0.9897677
## Ctrl:d10-50%:d0 NA NA NA NA
## 05%:d10-50%:d0 2.035306e+00 -3.44299786 7.513610 0.9991568
## 10%:d10-50%:d0 1.868640e+00 -3.60966453 7.346944 0.9997758
## 25%:d10-50%:d0 1.368640e+00 -4.10966453 6.846944 0.9999991
## 50%:d10-50%:d0 1.476241e+00 -4.00206361 6.954545 0.9999963
## Ctrl:d15-50%:d0 NA NA NA NA
## 05%:d15-50%:d0 2.368640e+00 -3.10966453 7.846944 0.9931231
## 10%:d15-50%:d0 2.035306e+00 -3.44299786 7.513610 0.9991568
## 25%:d15-50%:d0 2.368640e+00 -3.10966453 7.846944 0.9931231
## 50%:d15-50%:d0 4.476241e+00 -1.00206361 9.954545 0.2701835
## 05%:d05-Ctrl:d05 NA NA NA NA
## 10%:d05-Ctrl:d05 NA NA NA NA
## 25%:d05-Ctrl:d05 NA NA NA NA
## 50%:d05-Ctrl:d05 NA NA NA NA
## Ctrl:d10-Ctrl:d05 NA NA NA NA
## 05%:d10-Ctrl:d05 NA NA NA NA
## 10%:d10-Ctrl:d05 NA NA NA NA
## 25%:d10-Ctrl:d05 NA NA NA NA
## 50%:d10-Ctrl:d05 NA NA NA NA
## Ctrl:d15-Ctrl:d05 NA NA NA NA
## 05%:d15-Ctrl:d05 NA NA NA NA
## 10%:d15-Ctrl:d05 NA NA NA NA
## 25%:d15-Ctrl:d05 NA NA NA NA
## 50%:d15-Ctrl:d05 NA NA NA NA
## 10%:d05-05%:d05 -3.333333e-01 -4.80634995 4.139683 1.0000000
## 25%:d05-05%:d05 -8.333333e-01 -5.30634995 3.639683 1.0000000
## 50%:d05-05%:d05 5.492552e-01 -4.14208417 5.240595 1.0000000
## Ctrl:d10-05%:d05 NA NA NA NA
## 05%:d10-05%:d05 5.881287e-02 -4.41420375 4.531829 1.0000000
## 10%:d10-05%:d05 -1.078538e-01 -4.58087041 4.365163 1.0000000
## 25%:d10-05%:d05 -6.078538e-01 -5.08087041 3.865163 1.0000000
## 50%:d10-05%:d05 -5.002529e-01 -4.97326949 3.972764 1.0000000
## Ctrl:d15-05%:d05 NA NA NA NA
## 05%:d15-05%:d05 3.921462e-01 -4.08087041 4.865163 1.0000000
## 10%:d15-05%:d05 5.881287e-02 -4.41420375 4.531829 1.0000000
## 25%:d15-05%:d05 3.921462e-01 -4.08087041 4.865163 1.0000000
## 50%:d15-05%:d05 2.499747e+00 -1.97326949 6.972764 0.8966732
## 25%:d05-10%:d05 -5.000000e-01 -4.97301662 3.973017 1.0000000
## 50%:d05-10%:d05 8.825886e-01 -3.80875084 5.573928 1.0000000
## Ctrl:d10-10%:d05 NA NA NA NA
## 05%:d10-10%:d05 3.921462e-01 -4.08087041 4.865163 1.0000000
## 10%:d10-10%:d05 2.254795e-01 -4.24753708 4.698496 1.0000000
## 25%:d10-10%:d05 -2.745205e-01 -4.74753708 4.198496 1.0000000
## 50%:d10-10%:d05 -1.669195e-01 -4.63993616 4.306097 1.0000000
## Ctrl:d15-10%:d05 NA NA NA NA
## 05%:d15-10%:d05 7.254795e-01 -3.74753708 5.198496 1.0000000
## 10%:d15-10%:d05 3.921462e-01 -4.08087041 4.865163 1.0000000
## 25%:d15-10%:d05 7.254795e-01 -3.74753708 5.198496 1.0000000
## 50%:d15-10%:d05 2.833080e+00 -1.63993616 7.306097 0.7440334
## 50%:d05-25%:d05 1.382589e+00 -3.30875084 6.073928 0.9999811
## Ctrl:d10-25%:d05 NA NA NA NA
## 05%:d10-25%:d05 8.921462e-01 -3.58087041 5.365163 1.0000000
## 10%:d10-25%:d05 7.254795e-01 -3.74753708 5.198496 1.0000000
## 25%:d10-25%:d05 2.254795e-01 -4.24753708 4.698496 1.0000000
## 50%:d10-25%:d05 3.330805e-01 -4.13993616 4.806097 1.0000000
## Ctrl:d15-25%:d05 NA NA NA NA
## 05%:d15-25%:d05 1.225480e+00 -3.24753708 5.698496 0.9999950
## 10%:d15-25%:d05 8.921462e-01 -3.58087041 5.365163 1.0000000
## 25%:d15-25%:d05 1.225480e+00 -3.24753708 5.698496 0.9999950
## 50%:d15-25%:d05 3.333080e+00 -1.13993616 7.806097 0.4408384
## Ctrl:d10-50%:d05 NA NA NA NA
## 05%:d10-50%:d05 -4.904424e-01 -5.18178177 4.200897 1.0000000
## 10%:d10-50%:d05 -6.571090e-01 -5.34844844 4.034230 1.0000000
## 25%:d10-50%:d05 -1.157109e+00 -5.84844844 3.534230 0.9999993
## 50%:d10-50%:d05 -1.049508e+00 -5.74084752 3.641831 0.9999999
## Ctrl:d15-50%:d05 NA NA NA NA
## 05%:d15-50%:d05 -1.571090e-01 -4.84844844 4.534230 1.0000000
## 10%:d15-50%:d05 -4.904424e-01 -5.18178177 4.200897 1.0000000
## 25%:d15-50%:d05 -1.571090e-01 -4.84844844 4.534230 1.0000000
## 50%:d15-50%:d05 1.950492e+00 -2.74084752 6.641831 0.9958658
## 05%:d10-Ctrl:d10 NA NA NA NA
## 10%:d10-Ctrl:d10 NA NA NA NA
## 25%:d10-Ctrl:d10 NA NA NA NA
## 50%:d10-Ctrl:d10 NA NA NA NA
## Ctrl:d15-Ctrl:d10 NA NA NA NA
## 05%:d15-Ctrl:d10 NA NA NA NA
## 10%:d15-Ctrl:d10 NA NA NA NA
## 25%:d15-Ctrl:d10 NA NA NA NA
## 50%:d15-Ctrl:d10 NA NA NA NA
## 10%:d10-05%:d10 -1.666667e-01 -4.63968329 4.306350 1.0000000
## 25%:d10-05%:d10 -6.666667e-01 -5.13968329 3.806350 1.0000000
## 50%:d10-05%:d10 -5.590657e-01 -5.03208236 3.913951 1.0000000
## Ctrl:d15-05%:d10 NA NA NA NA
## 05%:d15-05%:d10 3.333333e-01 -4.13968329 4.806350 1.0000000
## 10%:d15-05%:d10 -1.776357e-15 -4.47301662 4.473017 1.0000000
## 25%:d15-05%:d10 3.333333e-01 -4.13968329 4.806350 1.0000000
## 50%:d15-05%:d10 2.440934e+00 -2.03208236 6.913951 0.9157193
## 25%:d10-10%:d10 -5.000000e-01 -4.97301662 3.973017 1.0000000
## 50%:d10-10%:d10 -3.923991e-01 -4.86541570 4.080618 1.0000000
## Ctrl:d15-10%:d10 NA NA NA NA
## 05%:d15-10%:d10 5.000000e-01 -3.97301662 4.973017 1.0000000
## 10%:d15-10%:d10 1.666667e-01 -4.30634995 4.639683 1.0000000
## 25%:d15-10%:d10 5.000000e-01 -3.97301662 4.973017 1.0000000
## 50%:d15-10%:d10 2.607601e+00 -1.86541570 7.080618 0.8552825
## 50%:d10-25%:d10 1.076009e-01 -4.36541570 4.580618 1.0000000
## Ctrl:d15-25%:d10 NA NA NA NA
## 05%:d15-25%:d10 1.000000e+00 -3.47301662 5.473017 0.9999999
## 10%:d15-25%:d10 6.666667e-01 -3.80634995 5.139683 1.0000000
## 25%:d15-25%:d10 1.000000e+00 -3.47301662 5.473017 0.9999999
## 50%:d15-25%:d10 3.107601e+00 -1.36541570 7.580618 0.5792720
## Ctrl:d15-50%:d10 NA NA NA NA
## 05%:d15-50%:d10 8.923991e-01 -3.58061754 5.365416 1.0000000
## 10%:d15-50%:d10 5.590657e-01 -3.91395087 5.032082 1.0000000
## 25%:d15-50%:d10 8.923991e-01 -3.58061754 5.365416 1.0000000
## 50%:d15-50%:d10 3.000000e+00 -1.47301662 7.473017 0.6459374
## 05%:d15-Ctrl:d15 NA NA NA NA
## 10%:d15-Ctrl:d15 NA NA NA NA
## 25%:d15-Ctrl:d15 NA NA NA NA
## 50%:d15-Ctrl:d15 NA NA NA NA
## 10%:d15-05%:d15 -3.333333e-01 -4.80634995 4.139683 1.0000000
## 25%:d15-05%:d15 -5.329071e-15 -4.47301662 4.473017 1.0000000
## 50%:d15-05%:d15 2.107601e+00 -2.36541570 6.580618 0.9807082
## 25%:d15-10%:d15 3.333333e-01 -4.13968329 4.806350 1.0000000
## 50%:d15-10%:d15 2.440934e+00 -2.03208236 6.913951 0.9157193
## 50%:d15-25%:d15 2.107601e+00 -2.36541570 6.580618 0.9807082
##
## $`Larval_stage:Treatment:Bti_application`
## diff lwr upr p adj
## Lx:Ctrl:none-L1:Ctrl:none 3.333333e+00 -3.61429133 10.2809580 0.9966607
## L1:05%:none-L1:Ctrl:none NA NA NA NA
## Lx:05%:none-L1:Ctrl:none NA NA NA NA
## L1:10%:none-L1:Ctrl:none NA NA NA NA
## Lx:10%:none-L1:Ctrl:none NA NA NA NA
## L1:25%:none-L1:Ctrl:none NA NA NA NA
## Lx:25%:none-L1:Ctrl:none NA NA NA NA
## L1:50%:none-L1:Ctrl:none NA NA NA NA
## Lx:50%:none-L1:Ctrl:none NA NA NA NA
## L1:Ctrl:d0-L1:Ctrl:none NA NA NA NA
## Lx:Ctrl:d0-L1:Ctrl:none NA NA NA NA
## L1:05%:d0-L1:Ctrl:none -3.333333e-01 -7.28095800 6.6142913 1.0000000
## Lx:05%:d0-L1:Ctrl:none 3.333333e+00 -3.61429133 10.2809580 0.9966607
## L1:10%:d0-L1:Ctrl:none -2.333333e+00 -9.28095800 4.6142913 0.9999993
## Lx:10%:d0-L1:Ctrl:none 1.333333e+00 -5.61429133 8.2809580 1.0000000
## L1:25%:d0-L1:Ctrl:none -3.333333e+00 -10.28095800 3.6142913 0.9966607
## Lx:25%:d0-L1:Ctrl:none 6.666667e-01 -6.28095800 7.6142913 1.0000000
## L1:50%:d0-L1:Ctrl:none NA NA NA NA
## Lx:50%:d0-L1:Ctrl:none 6.666667e-01 -6.28095800 7.6142913 1.0000000
## L1:Ctrl:d05-L1:Ctrl:none NA NA NA NA
## Lx:Ctrl:d05-L1:Ctrl:none NA NA NA NA
## L1:05%:d05-L1:Ctrl:none 7.105427e-15 -6.94762466 6.9476247 1.0000000
## Lx:05%:d05-L1:Ctrl:none 3.000000e+00 -3.94762466 9.9476247 0.9995830
## L1:10%:d05-L1:Ctrl:none 5.329071e-15 -6.94762466 6.9476247 1.0000000
## Lx:10%:d05-L1:Ctrl:none 2.333333e+00 -4.61429133 9.2809580 0.9999993
## L1:25%:d05-L1:Ctrl:none 5.329071e-15 -6.94762466 6.9476247 1.0000000
## Lx:25%:d05-L1:Ctrl:none 1.333333e+00 -5.61429133 8.2809580 1.0000000
## L1:50%:d05-L1:Ctrl:none 5.666667e+00 -2.10101385 13.4343472 0.5818083
## Lx:50%:d05-L1:Ctrl:none -3.333333e-01 -7.28095800 6.6142913 1.0000000
## L1:Ctrl:d10-L1:Ctrl:none NA NA NA NA
## Lx:Ctrl:d10-L1:Ctrl:none NA NA NA NA
## L1:05%:d10-L1:Ctrl:none 6.666667e-01 -6.28095800 7.6142913 1.0000000
## Lx:05%:d10-L1:Ctrl:none 2.333333e+00 -4.61429133 9.2809580 0.9999993
## L1:10%:d10-L1:Ctrl:none -3.333333e-01 -7.28095800 6.6142913 1.0000000
## Lx:10%:d10-L1:Ctrl:none 3.000000e+00 -3.94762466 9.9476247 0.9995830
## L1:25%:d10-L1:Ctrl:none -3.333333e-01 -7.28095800 6.6142913 1.0000000
## Lx:25%:d10-L1:Ctrl:none 2.000000e+00 -4.94762466 8.9476247 1.0000000
## L1:50%:d10-L1:Ctrl:none -1.666667e+00 -8.61429133 5.2809580 1.0000000
## Lx:50%:d10-L1:Ctrl:none 3.666667e+00 -3.28095800 10.6142913 0.9838739
## L1:Ctrl:d15-L1:Ctrl:none NA NA NA NA
## Lx:Ctrl:d15-L1:Ctrl:none NA NA NA NA
## L1:05%:d15-L1:Ctrl:none 1.333333e+00 -5.61429133 8.2809580 1.0000000
## Lx:05%:d15-L1:Ctrl:none 2.333333e+00 -4.61429133 9.2809580 0.9999993
## L1:10%:d15-L1:Ctrl:none 6.666667e-01 -6.28095800 7.6142913 1.0000000
## Lx:10%:d15-L1:Ctrl:none 2.333333e+00 -4.61429133 9.2809580 0.9999993
## L1:25%:d15-L1:Ctrl:none 1.333333e+00 -5.61429133 8.2809580 1.0000000
## Lx:25%:d15-L1:Ctrl:none 2.333333e+00 -4.61429133 9.2809580 0.9999993
## L1:50%:d15-L1:Ctrl:none 2.333333e+00 -4.61429133 9.2809580 0.9999993
## Lx:50%:d15-L1:Ctrl:none 5.666667e+00 -1.28095800 12.6142913 0.3253849
## L1:05%:none-Lx:Ctrl:none NA NA NA NA
## Lx:05%:none-Lx:Ctrl:none NA NA NA NA
## L1:10%:none-Lx:Ctrl:none NA NA NA NA
## Lx:10%:none-Lx:Ctrl:none NA NA NA NA
## L1:25%:none-Lx:Ctrl:none NA NA NA NA
## Lx:25%:none-Lx:Ctrl:none NA NA NA NA
## L1:50%:none-Lx:Ctrl:none NA NA NA NA
## Lx:50%:none-Lx:Ctrl:none NA NA NA NA
## L1:Ctrl:d0-Lx:Ctrl:none NA NA NA NA
## Lx:Ctrl:d0-Lx:Ctrl:none NA NA NA NA
## L1:05%:d0-Lx:Ctrl:none -3.666667e+00 -10.61429133 3.2809580 0.9838739
## Lx:05%:d0-Lx:Ctrl:none -7.993606e-15 -6.94762466 6.9476247 1.0000000
## L1:10%:d0-Lx:Ctrl:none -5.666667e+00 -12.61429133 1.2809580 0.3253849
## Lx:10%:d0-Lx:Ctrl:none -2.000000e+00 -8.94762466 4.9476247 1.0000000
## L1:25%:d0-Lx:Ctrl:none -6.666667e+00 -13.61429133 0.2809580 0.0800942
## Lx:25%:d0-Lx:Ctrl:none -2.666667e+00 -9.61429133 4.2809580 0.9999731
## L1:50%:d0-Lx:Ctrl:none NA NA NA NA
## Lx:50%:d0-Lx:Ctrl:none -2.666667e+00 -9.61429133 4.2809580 0.9999731
## L1:Ctrl:d05-Lx:Ctrl:none NA NA NA NA
## Lx:Ctrl:d05-Lx:Ctrl:none NA NA NA NA
## L1:05%:d05-Lx:Ctrl:none -3.333333e+00 -10.28095800 3.6142913 0.9966607
## Lx:05%:d05-Lx:Ctrl:none -3.333333e-01 -7.28095800 6.6142913 1.0000000
## L1:10%:d05-Lx:Ctrl:none -3.333333e+00 -10.28095800 3.6142913 0.9966607
## Lx:10%:d05-Lx:Ctrl:none -1.000000e+00 -7.94762466 5.9476247 1.0000000
## L1:25%:d05-Lx:Ctrl:none -3.333333e+00 -10.28095800 3.6142913 0.9966607
## Lx:25%:d05-Lx:Ctrl:none -2.000000e+00 -8.94762466 4.9476247 1.0000000
## L1:50%:d05-Lx:Ctrl:none 2.333333e+00 -5.43434718 10.1010138 1.0000000
## Lx:50%:d05-Lx:Ctrl:none -3.666667e+00 -10.61429133 3.2809580 0.9838739
## L1:Ctrl:d10-Lx:Ctrl:none NA NA NA NA
## Lx:Ctrl:d10-Lx:Ctrl:none NA NA NA NA
## L1:05%:d10-Lx:Ctrl:none -2.666667e+00 -9.61429133 4.2809580 0.9999731
## Lx:05%:d10-Lx:Ctrl:none -1.000000e+00 -7.94762466 5.9476247 1.0000000
## L1:10%:d10-Lx:Ctrl:none -3.666667e+00 -10.61429133 3.2809580 0.9838739
## Lx:10%:d10-Lx:Ctrl:none -3.333333e-01 -7.28095800 6.6142913 1.0000000
## L1:25%:d10-Lx:Ctrl:none -3.666667e+00 -10.61429133 3.2809580 0.9838739
## Lx:25%:d10-Lx:Ctrl:none -1.333333e+00 -8.28095800 5.6142913 1.0000000
## L1:50%:d10-Lx:Ctrl:none -5.000000e+00 -11.94762466 1.9476247 0.6130052
## Lx:50%:d10-Lx:Ctrl:none 3.333333e-01 -6.61429133 7.2809580 1.0000000
## L1:Ctrl:d15-Lx:Ctrl:none NA NA NA NA
## Lx:Ctrl:d15-Lx:Ctrl:none NA NA NA NA
## L1:05%:d15-Lx:Ctrl:none -2.000000e+00 -8.94762466 4.9476247 1.0000000
## Lx:05%:d15-Lx:Ctrl:none -1.000000e+00 -7.94762466 5.9476247 1.0000000
## L1:10%:d15-Lx:Ctrl:none -2.666667e+00 -9.61429133 4.2809580 0.9999731
## Lx:10%:d15-Lx:Ctrl:none -1.000000e+00 -7.94762466 5.9476247 1.0000000
## L1:25%:d15-Lx:Ctrl:none -2.000000e+00 -8.94762466 4.9476247 1.0000000
## Lx:25%:d15-Lx:Ctrl:none -1.000000e+00 -7.94762466 5.9476247 1.0000000
## L1:50%:d15-Lx:Ctrl:none -1.000000e+00 -7.94762466 5.9476247 1.0000000
## Lx:50%:d15-Lx:Ctrl:none 2.333333e+00 -4.61429133 9.2809580 0.9999993
## Lx:05%:none-L1:05%:none NA NA NA NA
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## Lx:50%:none-Lx:25%:none NA NA NA NA
## L1:Ctrl:d0-Lx:25%:none NA NA NA NA
## Lx:Ctrl:d0-Lx:25%:none NA NA NA NA
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## L1:Ctrl:d05-Lx:25%:none NA NA NA NA
## Lx:Ctrl:d05-Lx:25%:none NA NA NA NA
## L1:05%:d05-Lx:25%:none NA NA NA NA
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## L1:10%:d05-Lx:25%:none NA NA NA NA
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## L1:50%:d05-Lx:25%:none NA NA NA NA
## Lx:50%:d05-Lx:25%:none NA NA NA NA
## L1:Ctrl:d10-Lx:25%:none NA NA NA NA
## Lx:Ctrl:d10-Lx:25%:none NA NA NA NA
## L1:05%:d10-Lx:25%:none NA NA NA NA
## Lx:05%:d10-Lx:25%:none NA NA NA NA
## L1:10%:d10-Lx:25%:none NA NA NA NA
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## L1:50%:d10-Lx:25%:none NA NA NA NA
## Lx:50%:d10-Lx:25%:none NA NA NA NA
## L1:Ctrl:d15-Lx:25%:none NA NA NA NA
## Lx:Ctrl:d15-Lx:25%:none NA NA NA NA
## L1:05%:d15-Lx:25%:none NA NA NA NA
## Lx:05%:d15-Lx:25%:none NA NA NA NA
## L1:10%:d15-Lx:25%:none NA NA NA NA
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## Lx:50%:d15-Lx:25%:none NA NA NA NA
## Lx:50%:none-L1:50%:none NA NA NA NA
## L1:Ctrl:d0-L1:50%:none NA NA NA NA
## Lx:Ctrl:d0-L1:50%:none NA NA NA NA
## L1:05%:d0-L1:50%:none NA NA NA NA
## Lx:05%:d0-L1:50%:none NA NA NA NA
## L1:10%:d0-L1:50%:none NA NA NA NA
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## Lx:50%:d0-L1:50%:none NA NA NA NA
## L1:Ctrl:d05-L1:50%:none NA NA NA NA
## Lx:Ctrl:d05-L1:50%:none NA NA NA NA
## L1:05%:d05-L1:50%:none NA NA NA NA
## Lx:05%:d05-L1:50%:none NA NA NA NA
## L1:10%:d05-L1:50%:none NA NA NA NA
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## L1:50%:d05-L1:50%:none NA NA NA NA
## Lx:50%:d05-L1:50%:none NA NA NA NA
## L1:Ctrl:d10-L1:50%:none NA NA NA NA
## Lx:Ctrl:d10-L1:50%:none NA NA NA NA
## L1:05%:d10-L1:50%:none NA NA NA NA
## Lx:05%:d10-L1:50%:none NA NA NA NA
## L1:10%:d10-L1:50%:none NA NA NA NA
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## L1:50%:d10-L1:50%:none NA NA NA NA
## Lx:50%:d10-L1:50%:none NA NA NA NA
## L1:Ctrl:d15-L1:50%:none NA NA NA NA
## Lx:Ctrl:d15-L1:50%:none NA NA NA NA
## L1:05%:d15-L1:50%:none NA NA NA NA
## Lx:05%:d15-L1:50%:none NA NA NA NA
## L1:10%:d15-L1:50%:none NA NA NA NA
## Lx:10%:d15-L1:50%:none NA NA NA NA
## L1:25%:d15-L1:50%:none NA NA NA NA
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## L1:50%:d15-L1:50%:none NA NA NA NA
## Lx:50%:d15-L1:50%:none NA NA NA NA
## L1:Ctrl:d0-Lx:50%:none NA NA NA NA
## Lx:Ctrl:d0-Lx:50%:none NA NA NA NA
## L1:05%:d0-Lx:50%:none NA NA NA NA
## Lx:05%:d0-Lx:50%:none NA NA NA NA
## L1:10%:d0-Lx:50%:none NA NA NA NA
## Lx:10%:d0-Lx:50%:none NA NA NA NA
## L1:25%:d0-Lx:50%:none NA NA NA NA
## Lx:25%:d0-Lx:50%:none NA NA NA NA
## L1:50%:d0-Lx:50%:none NA NA NA NA
## Lx:50%:d0-Lx:50%:none NA NA NA NA
## L1:Ctrl:d05-Lx:50%:none NA NA NA NA
## Lx:Ctrl:d05-Lx:50%:none NA NA NA NA
## L1:05%:d05-Lx:50%:none NA NA NA NA
## Lx:05%:d05-Lx:50%:none NA NA NA NA
## L1:10%:d05-Lx:50%:none NA NA NA NA
## Lx:10%:d05-Lx:50%:none NA NA NA NA
## L1:25%:d05-Lx:50%:none NA NA NA NA
## Lx:25%:d05-Lx:50%:none NA NA NA NA
## L1:50%:d05-Lx:50%:none NA NA NA NA
## Lx:50%:d05-Lx:50%:none NA NA NA NA
## L1:Ctrl:d10-Lx:50%:none NA NA NA NA
## Lx:Ctrl:d10-Lx:50%:none NA NA NA NA
## L1:05%:d10-Lx:50%:none NA NA NA NA
## Lx:05%:d10-Lx:50%:none NA NA NA NA
## L1:10%:d10-Lx:50%:none NA NA NA NA
## Lx:10%:d10-Lx:50%:none NA NA NA NA
## L1:25%:d10-Lx:50%:none NA NA NA NA
## Lx:25%:d10-Lx:50%:none NA NA NA NA
## L1:50%:d10-Lx:50%:none NA NA NA NA
## Lx:50%:d10-Lx:50%:none NA NA NA NA
## L1:Ctrl:d15-Lx:50%:none NA NA NA NA
## Lx:Ctrl:d15-Lx:50%:none NA NA NA NA
## L1:05%:d15-Lx:50%:none NA NA NA NA
## Lx:05%:d15-Lx:50%:none NA NA NA NA
## L1:10%:d15-Lx:50%:none NA NA NA NA
## Lx:10%:d15-Lx:50%:none NA NA NA NA
## L1:25%:d15-Lx:50%:none NA NA NA NA
## Lx:25%:d15-Lx:50%:none NA NA NA NA
## L1:50%:d15-Lx:50%:none NA NA NA NA
## Lx:50%:d15-Lx:50%:none NA NA NA NA
## Lx:Ctrl:d0-L1:Ctrl:d0 NA NA NA NA
## L1:05%:d0-L1:Ctrl:d0 NA NA NA NA
## Lx:05%:d0-L1:Ctrl:d0 NA NA NA NA
## L1:10%:d0-L1:Ctrl:d0 NA NA NA NA
## Lx:10%:d0-L1:Ctrl:d0 NA NA NA NA
## L1:25%:d0-L1:Ctrl:d0 NA NA NA NA
## Lx:25%:d0-L1:Ctrl:d0 NA NA NA NA
## L1:50%:d0-L1:Ctrl:d0 NA NA NA NA
## Lx:50%:d0-L1:Ctrl:d0 NA NA NA NA
## L1:Ctrl:d05-L1:Ctrl:d0 NA NA NA NA
## Lx:Ctrl:d05-L1:Ctrl:d0 NA NA NA NA
## L1:05%:d05-L1:Ctrl:d0 NA NA NA NA
## Lx:05%:d05-L1:Ctrl:d0 NA NA NA NA
## L1:10%:d05-L1:Ctrl:d0 NA NA NA NA
## Lx:10%:d05-L1:Ctrl:d0 NA NA NA NA
## L1:25%:d05-L1:Ctrl:d0 NA NA NA NA
## Lx:25%:d05-L1:Ctrl:d0 NA NA NA NA
## L1:50%:d05-L1:Ctrl:d0 NA NA NA NA
## Lx:50%:d05-L1:Ctrl:d0 NA NA NA NA
## L1:Ctrl:d10-L1:Ctrl:d0 NA NA NA NA
## Lx:Ctrl:d10-L1:Ctrl:d0 NA NA NA NA
## L1:05%:d10-L1:Ctrl:d0 NA NA NA NA
## Lx:05%:d10-L1:Ctrl:d0 NA NA NA NA
## L1:10%:d10-L1:Ctrl:d0 NA NA NA NA
## Lx:10%:d10-L1:Ctrl:d0 NA NA NA NA
## L1:25%:d10-L1:Ctrl:d0 NA NA NA NA
## Lx:25%:d10-L1:Ctrl:d0 NA NA NA NA
## L1:50%:d10-L1:Ctrl:d0 NA NA NA NA
## Lx:50%:d10-L1:Ctrl:d0 NA NA NA NA
## L1:Ctrl:d15-L1:Ctrl:d0 NA NA NA NA
## Lx:Ctrl:d15-L1:Ctrl:d0 NA NA NA NA
## L1:05%:d15-L1:Ctrl:d0 NA NA NA NA
## Lx:05%:d15-L1:Ctrl:d0 NA NA NA NA
## L1:10%:d15-L1:Ctrl:d0 NA NA NA NA
## Lx:10%:d15-L1:Ctrl:d0 NA NA NA NA
## L1:25%:d15-L1:Ctrl:d0 NA NA NA NA
## Lx:25%:d15-L1:Ctrl:d0 NA NA NA NA
## L1:50%:d15-L1:Ctrl:d0 NA NA NA NA
## Lx:50%:d15-L1:Ctrl:d0 NA NA NA NA
## L1:05%:d0-Lx:Ctrl:d0 NA NA NA NA
## Lx:05%:d0-Lx:Ctrl:d0 NA NA NA NA
## L1:10%:d0-Lx:Ctrl:d0 NA NA NA NA
## Lx:10%:d0-Lx:Ctrl:d0 NA NA NA NA
## L1:25%:d0-Lx:Ctrl:d0 NA NA NA NA
## Lx:25%:d0-Lx:Ctrl:d0 NA NA NA NA
## L1:50%:d0-Lx:Ctrl:d0 NA NA NA NA
## Lx:50%:d0-Lx:Ctrl:d0 NA NA NA NA
## L1:Ctrl:d05-Lx:Ctrl:d0 NA NA NA NA
## Lx:Ctrl:d05-Lx:Ctrl:d0 NA NA NA NA
## L1:05%:d05-Lx:Ctrl:d0 NA NA NA NA
## Lx:05%:d05-Lx:Ctrl:d0 NA NA NA NA
## L1:10%:d05-Lx:Ctrl:d0 NA NA NA NA
## Lx:10%:d05-Lx:Ctrl:d0 NA NA NA NA
## L1:25%:d05-Lx:Ctrl:d0 NA NA NA NA
## Lx:25%:d05-Lx:Ctrl:d0 NA NA NA NA
## L1:50%:d05-Lx:Ctrl:d0 NA NA NA NA
## Lx:50%:d05-Lx:Ctrl:d0 NA NA NA NA
## L1:Ctrl:d10-Lx:Ctrl:d0 NA NA NA NA
## Lx:Ctrl:d10-Lx:Ctrl:d0 NA NA NA NA
## L1:05%:d10-Lx:Ctrl:d0 NA NA NA NA
## Lx:05%:d10-Lx:Ctrl:d0 NA NA NA NA
## L1:10%:d10-Lx:Ctrl:d0 NA NA NA NA
## Lx:10%:d10-Lx:Ctrl:d0 NA NA NA NA
## L1:25%:d10-Lx:Ctrl:d0 NA NA NA NA
## Lx:25%:d10-Lx:Ctrl:d0 NA NA NA NA
## L1:50%:d10-Lx:Ctrl:d0 NA NA NA NA
## Lx:50%:d10-Lx:Ctrl:d0 NA NA NA NA
## L1:Ctrl:d15-Lx:Ctrl:d0 NA NA NA NA
## Lx:Ctrl:d15-Lx:Ctrl:d0 NA NA NA NA
## L1:05%:d15-Lx:Ctrl:d0 NA NA NA NA
## Lx:05%:d15-Lx:Ctrl:d0 NA NA NA NA
## L1:10%:d15-Lx:Ctrl:d0 NA NA NA NA
## Lx:10%:d15-Lx:Ctrl:d0 NA NA NA NA
## L1:25%:d15-Lx:Ctrl:d0 NA NA NA NA
## Lx:25%:d15-Lx:Ctrl:d0 NA NA NA NA
## L1:50%:d15-Lx:Ctrl:d0 NA NA NA NA
## Lx:50%:d15-Lx:Ctrl:d0 NA NA NA NA
## Lx:05%:d0-L1:05%:d0 3.666667e+00 -3.28095800 10.6142913 0.9838739
## L1:10%:d0-L1:05%:d0 -2.000000e+00 -8.94762466 4.9476247 1.0000000
## Lx:10%:d0-L1:05%:d0 1.666667e+00 -5.28095800 8.6142913 1.0000000
## L1:25%:d0-L1:05%:d0 -3.000000e+00 -9.94762466 3.9476247 0.9995830
## Lx:25%:d0-L1:05%:d0 1.000000e+00 -5.94762466 7.9476247 1.0000000
## L1:50%:d0-L1:05%:d0 NA NA NA NA
## Lx:50%:d0-L1:05%:d0 1.000000e+00 -5.94762466 7.9476247 1.0000000
## L1:Ctrl:d05-L1:05%:d0 NA NA NA NA
## Lx:Ctrl:d05-L1:05%:d0 NA NA NA NA
## L1:05%:d05-L1:05%:d0 3.333333e-01 -6.61429133 7.2809580 1.0000000
## Lx:05%:d05-L1:05%:d0 3.333333e+00 -3.61429133 10.2809580 0.9966607
## L1:10%:d05-L1:05%:d0 3.333333e-01 -6.61429133 7.2809580 1.0000000
## Lx:10%:d05-L1:05%:d0 2.666667e+00 -4.28095800 9.6142913 0.9999731
## L1:25%:d05-L1:05%:d0 3.333333e-01 -6.61429133 7.2809580 1.0000000
## Lx:25%:d05-L1:05%:d0 1.666667e+00 -5.28095800 8.6142913 1.0000000
## L1:50%:d05-L1:05%:d0 6.000000e+00 -1.76768052 13.7676805 0.4475651
## Lx:50%:d05-L1:05%:d0 8.881784e-16 -6.94762466 6.9476247 1.0000000
## L1:Ctrl:d10-L1:05%:d0 NA NA NA NA
## Lx:Ctrl:d10-L1:05%:d0 NA NA NA NA
## L1:05%:d10-L1:05%:d0 1.000000e+00 -5.94762466 7.9476247 1.0000000
## Lx:05%:d10-L1:05%:d0 2.666667e+00 -4.28095800 9.6142913 0.9999731
## L1:10%:d10-L1:05%:d0 2.220446e-15 -6.94762466 6.9476247 1.0000000
## Lx:10%:d10-L1:05%:d0 3.333333e+00 -3.61429133 10.2809580 0.9966607
## L1:25%:d10-L1:05%:d0 4.440892e-15 -6.94762466 6.9476247 1.0000000
## Lx:25%:d10-L1:05%:d0 2.333333e+00 -4.61429133 9.2809580 0.9999993
## L1:50%:d10-L1:05%:d0 -1.333333e+00 -8.28095800 5.6142913 1.0000000
## Lx:50%:d10-L1:05%:d0 4.000000e+00 -2.94762466 10.9476247 0.9473193
## L1:Ctrl:d15-L1:05%:d0 NA NA NA NA
## Lx:Ctrl:d15-L1:05%:d0 NA NA NA NA
## L1:05%:d15-L1:05%:d0 1.666667e+00 -5.28095800 8.6142913 1.0000000
## Lx:05%:d15-L1:05%:d0 2.666667e+00 -4.28095800 9.6142913 0.9999731
## L1:10%:d15-L1:05%:d0 1.000000e+00 -5.94762466 7.9476247 1.0000000
## Lx:10%:d15-L1:05%:d0 2.666667e+00 -4.28095800 9.6142913 0.9999731
## L1:25%:d15-L1:05%:d0 1.666667e+00 -5.28095800 8.6142913 1.0000000
## Lx:25%:d15-L1:05%:d0 2.666667e+00 -4.28095800 9.6142913 0.9999731
## L1:50%:d15-L1:05%:d0 2.666667e+00 -4.28095800 9.6142913 0.9999731
## Lx:50%:d15-L1:05%:d0 6.000000e+00 -0.94762466 12.9476247 0.2152019
## L1:10%:d0-Lx:05%:d0 -5.666667e+00 -12.61429133 1.2809580 0.3253849
## Lx:10%:d0-Lx:05%:d0 -2.000000e+00 -8.94762466 4.9476247 1.0000000
## L1:25%:d0-Lx:05%:d0 -6.666667e+00 -13.61429133 0.2809580 0.0800942
## Lx:25%:d0-Lx:05%:d0 -2.666667e+00 -9.61429133 4.2809580 0.9999731
## L1:50%:d0-Lx:05%:d0 NA NA NA NA
## Lx:50%:d0-Lx:05%:d0 -2.666667e+00 -9.61429133 4.2809580 0.9999731
## L1:Ctrl:d05-Lx:05%:d0 NA NA NA NA
## Lx:Ctrl:d05-Lx:05%:d0 NA NA NA NA
## L1:05%:d05-Lx:05%:d0 -3.333333e+00 -10.28095800 3.6142913 0.9966607
## Lx:05%:d05-Lx:05%:d0 -3.333333e-01 -7.28095800 6.6142913 1.0000000
## L1:10%:d05-Lx:05%:d0 -3.333333e+00 -10.28095800 3.6142913 0.9966607
## Lx:10%:d05-Lx:05%:d0 -1.000000e+00 -7.94762466 5.9476247 1.0000000
## L1:25%:d05-Lx:05%:d0 -3.333333e+00 -10.28095800 3.6142913 0.9966607
## Lx:25%:d05-Lx:05%:d0 -2.000000e+00 -8.94762466 4.9476247 1.0000000
## L1:50%:d05-Lx:05%:d0 2.333333e+00 -5.43434718 10.1010138 1.0000000
## Lx:50%:d05-Lx:05%:d0 -3.666667e+00 -10.61429133 3.2809580 0.9838739
## L1:Ctrl:d10-Lx:05%:d0 NA NA NA NA
## Lx:Ctrl:d10-Lx:05%:d0 NA NA NA NA
## L1:05%:d10-Lx:05%:d0 -2.666667e+00 -9.61429133 4.2809580 0.9999731
## Lx:05%:d10-Lx:05%:d0 -1.000000e+00 -7.94762466 5.9476247 1.0000000
## L1:10%:d10-Lx:05%:d0 -3.666667e+00 -10.61429133 3.2809580 0.9838739
## Lx:10%:d10-Lx:05%:d0 -3.333333e-01 -7.28095800 6.6142913 1.0000000
## L1:25%:d10-Lx:05%:d0 -3.666667e+00 -10.61429133 3.2809580 0.9838739
## Lx:25%:d10-Lx:05%:d0 -1.333333e+00 -8.28095800 5.6142913 1.0000000
## L1:50%:d10-Lx:05%:d0 -5.000000e+00 -11.94762466 1.9476247 0.6130052
## Lx:50%:d10-Lx:05%:d0 3.333333e-01 -6.61429133 7.2809580 1.0000000
## L1:Ctrl:d15-Lx:05%:d0 NA NA NA NA
## Lx:Ctrl:d15-Lx:05%:d0 NA NA NA NA
## L1:05%:d15-Lx:05%:d0 -2.000000e+00 -8.94762466 4.9476247 1.0000000
## Lx:05%:d15-Lx:05%:d0 -1.000000e+00 -7.94762466 5.9476247 1.0000000
## L1:10%:d15-Lx:05%:d0 -2.666667e+00 -9.61429133 4.2809580 0.9999731
## Lx:10%:d15-Lx:05%:d0 -1.000000e+00 -7.94762466 5.9476247 1.0000000
## L1:25%:d15-Lx:05%:d0 -2.000000e+00 -8.94762466 4.9476247 1.0000000
## Lx:25%:d15-Lx:05%:d0 -1.000000e+00 -7.94762466 5.9476247 1.0000000
## L1:50%:d15-Lx:05%:d0 -1.000000e+00 -7.94762466 5.9476247 1.0000000
## Lx:50%:d15-Lx:05%:d0 2.333333e+00 -4.61429133 9.2809580 0.9999993
## Lx:10%:d0-L1:10%:d0 3.666667e+00 -3.28095800 10.6142913 0.9838739
## L1:25%:d0-L1:10%:d0 -1.000000e+00 -7.94762466 5.9476247 1.0000000
## Lx:25%:d0-L1:10%:d0 3.000000e+00 -3.94762466 9.9476247 0.9995830
## L1:50%:d0-L1:10%:d0 NA NA NA NA
## Lx:50%:d0-L1:10%:d0 3.000000e+00 -3.94762466 9.9476247 0.9995830
## L1:Ctrl:d05-L1:10%:d0 NA NA NA NA
## Lx:Ctrl:d05-L1:10%:d0 NA NA NA NA
## L1:05%:d05-L1:10%:d0 2.333333e+00 -4.61429133 9.2809580 0.9999993
## Lx:05%:d05-L1:10%:d0 5.333333e+00 -1.61429133 12.2809580 0.4621129
## L1:10%:d05-L1:10%:d0 2.333333e+00 -4.61429133 9.2809580 0.9999993
## Lx:10%:d05-L1:10%:d0 4.666667e+00 -2.28095800 11.6142913 0.7572674
## L1:25%:d05-L1:10%:d0 2.333333e+00 -4.61429133 9.2809580 0.9999993
## Lx:25%:d05-L1:10%:d0 3.666667e+00 -3.28095800 10.6142913 0.9838739
## L1:50%:d05-L1:10%:d0 8.000000e+00 0.23231948 15.7676805 0.0346666
## Lx:50%:d05-L1:10%:d0 2.000000e+00 -4.94762466 8.9476247 1.0000000
## L1:Ctrl:d10-L1:10%:d0 NA NA NA NA
## Lx:Ctrl:d10-L1:10%:d0 NA NA NA NA
## L1:05%:d10-L1:10%:d0 3.000000e+00 -3.94762466 9.9476247 0.9995830
## Lx:05%:d10-L1:10%:d0 4.666667e+00 -2.28095800 11.6142913 0.7572674
## L1:10%:d10-L1:10%:d0 2.000000e+00 -4.94762466 8.9476247 1.0000000
## Lx:10%:d10-L1:10%:d0 5.333333e+00 -1.61429133 12.2809580 0.4621129
## L1:25%:d10-L1:10%:d0 2.000000e+00 -4.94762466 8.9476247 1.0000000
## Lx:25%:d10-L1:10%:d0 4.333333e+00 -2.61429133 11.2809580 0.8728649
## L1:50%:d10-L1:10%:d0 6.666667e-01 -6.28095800 7.6142913 1.0000000
## Lx:50%:d10-L1:10%:d0 6.000000e+00 -0.94762466 12.9476247 0.2152019
## L1:Ctrl:d15-L1:10%:d0 NA NA NA NA
## Lx:Ctrl:d15-L1:10%:d0 NA NA NA NA
## L1:05%:d15-L1:10%:d0 3.666667e+00 -3.28095800 10.6142913 0.9838739
## Lx:05%:d15-L1:10%:d0 4.666667e+00 -2.28095800 11.6142913 0.7572674
## L1:10%:d15-L1:10%:d0 3.000000e+00 -3.94762466 9.9476247 0.9995830
## Lx:10%:d15-L1:10%:d0 4.666667e+00 -2.28095800 11.6142913 0.7572674
## L1:25%:d15-L1:10%:d0 3.666667e+00 -3.28095800 10.6142913 0.9838739
## Lx:25%:d15-L1:10%:d0 4.666667e+00 -2.28095800 11.6142913 0.7572674
## L1:50%:d15-L1:10%:d0 4.666667e+00 -2.28095800 11.6142913 0.7572674
## Lx:50%:d15-L1:10%:d0 8.000000e+00 1.05237534 14.9476247 0.0068727
## L1:25%:d0-Lx:10%:d0 -4.666667e+00 -11.61429133 2.2809580 0.7572674
## Lx:25%:d0-Lx:10%:d0 -6.666667e-01 -7.61429133 6.2809580 1.0000000
## L1:50%:d0-Lx:10%:d0 NA NA NA NA
## Lx:50%:d0-Lx:10%:d0 -6.666667e-01 -7.61429133 6.2809580 1.0000000
## L1:Ctrl:d05-Lx:10%:d0 NA NA NA NA
## Lx:Ctrl:d05-Lx:10%:d0 NA NA NA NA
## L1:05%:d05-Lx:10%:d0 -1.333333e+00 -8.28095800 5.6142913 1.0000000
## Lx:05%:d05-Lx:10%:d0 1.666667e+00 -5.28095800 8.6142913 1.0000000
## L1:10%:d05-Lx:10%:d0 -1.333333e+00 -8.28095800 5.6142913 1.0000000
## Lx:10%:d05-Lx:10%:d0 1.000000e+00 -5.94762466 7.9476247 1.0000000
## L1:25%:d05-Lx:10%:d0 -1.333333e+00 -8.28095800 5.6142913 1.0000000
## Lx:25%:d05-Lx:10%:d0 5.329071e-15 -6.94762466 6.9476247 1.0000000
## L1:50%:d05-Lx:10%:d0 4.333333e+00 -3.43434718 12.1010138 0.9647329
## Lx:50%:d05-Lx:10%:d0 -1.666667e+00 -8.61429133 5.2809580 1.0000000
## L1:Ctrl:d10-Lx:10%:d0 NA NA NA NA
## Lx:Ctrl:d10-Lx:10%:d0 NA NA NA NA
## L1:05%:d10-Lx:10%:d0 -6.666667e-01 -7.61429133 6.2809580 1.0000000
## Lx:05%:d10-Lx:10%:d0 1.000000e+00 -5.94762466 7.9476247 1.0000000
## L1:10%:d10-Lx:10%:d0 -1.666667e+00 -8.61429133 5.2809580 1.0000000
## Lx:10%:d10-Lx:10%:d0 1.666667e+00 -5.28095800 8.6142913 1.0000000
## L1:25%:d10-Lx:10%:d0 -1.666667e+00 -8.61429133 5.2809580 1.0000000
## Lx:25%:d10-Lx:10%:d0 6.666667e-01 -6.28095800 7.6142913 1.0000000
## L1:50%:d10-Lx:10%:d0 -3.000000e+00 -9.94762466 3.9476247 0.9995830
## Lx:50%:d10-Lx:10%:d0 2.333333e+00 -4.61429133 9.2809580 0.9999993
## L1:Ctrl:d15-Lx:10%:d0 NA NA NA NA
## Lx:Ctrl:d15-Lx:10%:d0 NA NA NA NA
## L1:05%:d15-Lx:10%:d0 1.776357e-15 -6.94762466 6.9476247 1.0000000
## Lx:05%:d15-Lx:10%:d0 1.000000e+00 -5.94762466 7.9476247 1.0000000
## L1:10%:d15-Lx:10%:d0 -6.666667e-01 -7.61429133 6.2809580 1.0000000
## Lx:10%:d15-Lx:10%:d0 1.000000e+00 -5.94762466 7.9476247 1.0000000
## L1:25%:d15-Lx:10%:d0 -2.664535e-15 -6.94762466 6.9476247 1.0000000
## Lx:25%:d15-Lx:10%:d0 1.000000e+00 -5.94762466 7.9476247 1.0000000
## L1:50%:d15-Lx:10%:d0 1.000000e+00 -5.94762466 7.9476247 1.0000000
## Lx:50%:d15-Lx:10%:d0 4.333333e+00 -2.61429133 11.2809580 0.8728649
## Lx:25%:d0-L1:25%:d0 4.000000e+00 -2.94762466 10.9476247 0.9473193
## L1:50%:d0-L1:25%:d0 NA NA NA NA
## Lx:50%:d0-L1:25%:d0 4.000000e+00 -2.94762466 10.9476247 0.9473193
## L1:Ctrl:d05-L1:25%:d0 NA NA NA NA
## Lx:Ctrl:d05-L1:25%:d0 NA NA NA NA
## L1:05%:d05-L1:25%:d0 3.333333e+00 -3.61429133 10.2809580 0.9966607
## Lx:05%:d05-L1:25%:d0 6.333333e+00 -0.61429133 13.2809580 0.1345722
## L1:10%:d05-L1:25%:d0 3.333333e+00 -3.61429133 10.2809580 0.9966607
## Lx:10%:d05-L1:25%:d0 5.666667e+00 -1.28095800 12.6142913 0.3253849
## L1:25%:d05-L1:25%:d0 3.333333e+00 -3.61429133 10.2809580 0.9966607
## Lx:25%:d05-L1:25%:d0 4.666667e+00 -2.28095800 11.6142913 0.7572674
## L1:50%:d05-L1:25%:d0 9.000000e+00 1.23231948 16.7676805 0.0062120
## Lx:50%:d05-L1:25%:d0 3.000000e+00 -3.94762466 9.9476247 0.9995830
## L1:Ctrl:d10-L1:25%:d0 NA NA NA NA
## Lx:Ctrl:d10-L1:25%:d0 NA NA NA NA
## L1:05%:d10-L1:25%:d0 4.000000e+00 -2.94762466 10.9476247 0.9473193
## Lx:05%:d10-L1:25%:d0 5.666667e+00 -1.28095800 12.6142913 0.3253849
## L1:10%:d10-L1:25%:d0 3.000000e+00 -3.94762466 9.9476247 0.9995830
## Lx:10%:d10-L1:25%:d0 6.333333e+00 -0.61429133 13.2809580 0.1345722
## L1:25%:d10-L1:25%:d0 3.000000e+00 -3.94762466 9.9476247 0.9995830
## Lx:25%:d10-L1:25%:d0 5.333333e+00 -1.61429133 12.2809580 0.4621129
## L1:50%:d10-L1:25%:d0 1.666667e+00 -5.28095800 8.6142913 1.0000000
## Lx:50%:d10-L1:25%:d0 7.000000e+00 0.05237534 13.9476247 0.0456529
## L1:Ctrl:d15-L1:25%:d0 NA NA NA NA
## Lx:Ctrl:d15-L1:25%:d0 NA NA NA NA
## L1:05%:d15-L1:25%:d0 4.666667e+00 -2.28095800 11.6142913 0.7572674
## Lx:05%:d15-L1:25%:d0 5.666667e+00 -1.28095800 12.6142913 0.3253849
## L1:10%:d15-L1:25%:d0 4.000000e+00 -2.94762466 10.9476247 0.9473193
## Lx:10%:d15-L1:25%:d0 5.666667e+00 -1.28095800 12.6142913 0.3253849
## L1:25%:d15-L1:25%:d0 4.666667e+00 -2.28095800 11.6142913 0.7572674
## Lx:25%:d15-L1:25%:d0 5.666667e+00 -1.28095800 12.6142913 0.3253849
## L1:50%:d15-L1:25%:d0 5.666667e+00 -1.28095800 12.6142913 0.3253849
## Lx:50%:d15-L1:25%:d0 9.000000e+00 2.05237534 15.9476247 0.0008255
## L1:50%:d0-Lx:25%:d0 NA NA NA NA
## Lx:50%:d0-Lx:25%:d0 -8.881784e-16 -6.94762466 6.9476247 1.0000000
## L1:Ctrl:d05-Lx:25%:d0 NA NA NA NA
## Lx:Ctrl:d05-Lx:25%:d0 NA NA NA NA
## L1:05%:d05-Lx:25%:d0 -6.666667e-01 -7.61429133 6.2809580 1.0000000
## Lx:05%:d05-Lx:25%:d0 2.333333e+00 -4.61429133 9.2809580 0.9999993
## L1:10%:d05-Lx:25%:d0 -6.666667e-01 -7.61429133 6.2809580 1.0000000
## Lx:10%:d05-Lx:25%:d0 1.666667e+00 -5.28095800 8.6142913 1.0000000
## L1:25%:d05-Lx:25%:d0 -6.666667e-01 -7.61429133 6.2809580 1.0000000
## Lx:25%:d05-Lx:25%:d0 6.666667e-01 -6.28095800 7.6142913 1.0000000
## L1:50%:d05-Lx:25%:d0 5.000000e+00 -2.76768052 12.7676805 0.8293475
## Lx:50%:d05-Lx:25%:d0 -1.000000e+00 -7.94762466 5.9476247 1.0000000
## L1:Ctrl:d10-Lx:25%:d0 NA NA NA NA
## Lx:Ctrl:d10-Lx:25%:d0 NA NA NA NA
## L1:05%:d10-Lx:25%:d0 -8.881784e-16 -6.94762466 6.9476247 1.0000000
## Lx:05%:d10-Lx:25%:d0 1.666667e+00 -5.28095800 8.6142913 1.0000000
## L1:10%:d10-Lx:25%:d0 -1.000000e+00 -7.94762466 5.9476247 1.0000000
## Lx:10%:d10-Lx:25%:d0 2.333333e+00 -4.61429133 9.2809580 0.9999993
## L1:25%:d10-Lx:25%:d0 -1.000000e+00 -7.94762466 5.9476247 1.0000000
## Lx:25%:d10-Lx:25%:d0 1.333333e+00 -5.61429133 8.2809580 1.0000000
## L1:50%:d10-Lx:25%:d0 -2.333333e+00 -9.28095800 4.6142913 0.9999993
## Lx:50%:d10-Lx:25%:d0 3.000000e+00 -3.94762466 9.9476247 0.9995830
## L1:Ctrl:d15-Lx:25%:d0 NA NA NA NA
## Lx:Ctrl:d15-Lx:25%:d0 NA NA NA NA
## L1:05%:d15-Lx:25%:d0 6.666667e-01 -6.28095800 7.6142913 1.0000000
## Lx:05%:d15-Lx:25%:d0 1.666667e+00 -5.28095800 8.6142913 1.0000000
## L1:10%:d15-Lx:25%:d0 -4.440892e-15 -6.94762466 6.9476247 1.0000000
## Lx:10%:d15-Lx:25%:d0 1.666667e+00 -5.28095800 8.6142913 1.0000000
## L1:25%:d15-Lx:25%:d0 6.666667e-01 -6.28095800 7.6142913 1.0000000
## Lx:25%:d15-Lx:25%:d0 1.666667e+00 -5.28095800 8.6142913 1.0000000
## L1:50%:d15-Lx:25%:d0 1.666667e+00 -5.28095800 8.6142913 1.0000000
## Lx:50%:d15-Lx:25%:d0 5.000000e+00 -1.94762466 11.9476247 0.6130052
## Lx:50%:d0-L1:50%:d0 NA NA NA NA
## L1:Ctrl:d05-L1:50%:d0 NA NA NA NA
## Lx:Ctrl:d05-L1:50%:d0 NA NA NA NA
## L1:05%:d05-L1:50%:d0 NA NA NA NA
## Lx:05%:d05-L1:50%:d0 NA NA NA NA
## L1:10%:d05-L1:50%:d0 NA NA NA NA
## Lx:10%:d05-L1:50%:d0 NA NA NA NA
## L1:25%:d05-L1:50%:d0 NA NA NA NA
## Lx:25%:d05-L1:50%:d0 NA NA NA NA
## L1:50%:d05-L1:50%:d0 NA NA NA NA
## Lx:50%:d05-L1:50%:d0 NA NA NA NA
## L1:Ctrl:d10-L1:50%:d0 NA NA NA NA
## Lx:Ctrl:d10-L1:50%:d0 NA NA NA NA
## L1:05%:d10-L1:50%:d0 NA NA NA NA
## Lx:05%:d10-L1:50%:d0 NA NA NA NA
## L1:10%:d10-L1:50%:d0 NA NA NA NA
## Lx:10%:d10-L1:50%:d0 NA NA NA NA
## L1:25%:d10-L1:50%:d0 NA NA NA NA
## Lx:25%:d10-L1:50%:d0 NA NA NA NA
## L1:50%:d10-L1:50%:d0 NA NA NA NA
## Lx:50%:d10-L1:50%:d0 NA NA NA NA
## L1:Ctrl:d15-L1:50%:d0 NA NA NA NA
## Lx:Ctrl:d15-L1:50%:d0 NA NA NA NA
## L1:05%:d15-L1:50%:d0 NA NA NA NA
## Lx:05%:d15-L1:50%:d0 NA NA NA NA
## L1:10%:d15-L1:50%:d0 NA NA NA NA
## Lx:10%:d15-L1:50%:d0 NA NA NA NA
## L1:25%:d15-L1:50%:d0 NA NA NA NA
## Lx:25%:d15-L1:50%:d0 NA NA NA NA
## L1:50%:d15-L1:50%:d0 NA NA NA NA
## Lx:50%:d15-L1:50%:d0 NA NA NA NA
## L1:Ctrl:d05-Lx:50%:d0 NA NA NA NA
## Lx:Ctrl:d05-Lx:50%:d0 NA NA NA NA
## L1:05%:d05-Lx:50%:d0 -6.666667e-01 -7.61429133 6.2809580 1.0000000
## Lx:05%:d05-Lx:50%:d0 2.333333e+00 -4.61429133 9.2809580 0.9999993
## L1:10%:d05-Lx:50%:d0 -6.666667e-01 -7.61429133 6.2809580 1.0000000
## Lx:10%:d05-Lx:50%:d0 1.666667e+00 -5.28095800 8.6142913 1.0000000
## L1:25%:d05-Lx:50%:d0 -6.666667e-01 -7.61429133 6.2809580 1.0000000
## Lx:25%:d05-Lx:50%:d0 6.666667e-01 -6.28095800 7.6142913 1.0000000
## L1:50%:d05-Lx:50%:d0 5.000000e+00 -2.76768052 12.7676805 0.8293475
## Lx:50%:d05-Lx:50%:d0 -1.000000e+00 -7.94762466 5.9476247 1.0000000
## L1:Ctrl:d10-Lx:50%:d0 NA NA NA NA
## Lx:Ctrl:d10-Lx:50%:d0 NA NA NA NA
## L1:05%:d10-Lx:50%:d0 0.000000e+00 -6.94762466 6.9476247 1.0000000
## Lx:05%:d10-Lx:50%:d0 1.666667e+00 -5.28095800 8.6142913 1.0000000
## L1:10%:d10-Lx:50%:d0 -1.000000e+00 -7.94762466 5.9476247 1.0000000
## Lx:10%:d10-Lx:50%:d0 2.333333e+00 -4.61429133 9.2809580 0.9999993
## L1:25%:d10-Lx:50%:d0 -1.000000e+00 -7.94762466 5.9476247 1.0000000
## Lx:25%:d10-Lx:50%:d0 1.333333e+00 -5.61429133 8.2809580 1.0000000
## L1:50%:d10-Lx:50%:d0 -2.333333e+00 -9.28095800 4.6142913 0.9999993
## Lx:50%:d10-Lx:50%:d0 3.000000e+00 -3.94762466 9.9476247 0.9995830
## L1:Ctrl:d15-Lx:50%:d0 NA NA NA NA
## Lx:Ctrl:d15-Lx:50%:d0 NA NA NA NA
## L1:05%:d15-Lx:50%:d0 6.666667e-01 -6.28095800 7.6142913 1.0000000
## Lx:05%:d15-Lx:50%:d0 1.666667e+00 -5.28095800 8.6142913 1.0000000
## L1:10%:d15-Lx:50%:d0 -3.552714e-15 -6.94762466 6.9476247 1.0000000
## Lx:10%:d15-Lx:50%:d0 1.666667e+00 -5.28095800 8.6142913 1.0000000
## L1:25%:d15-Lx:50%:d0 6.666667e-01 -6.28095800 7.6142913 1.0000000
## Lx:25%:d15-Lx:50%:d0 1.666667e+00 -5.28095800 8.6142913 1.0000000
## L1:50%:d15-Lx:50%:d0 1.666667e+00 -5.28095800 8.6142913 1.0000000
## Lx:50%:d15-Lx:50%:d0 5.000000e+00 -1.94762466 11.9476247 0.6130052
## Lx:Ctrl:d05-L1:Ctrl:d05 NA NA NA NA
## L1:05%:d05-L1:Ctrl:d05 NA NA NA NA
## Lx:05%:d05-L1:Ctrl:d05 NA NA NA NA
## L1:10%:d05-L1:Ctrl:d05 NA NA NA NA
## Lx:10%:d05-L1:Ctrl:d05 NA NA NA NA
## L1:25%:d05-L1:Ctrl:d05 NA NA NA NA
## Lx:25%:d05-L1:Ctrl:d05 NA NA NA NA
## L1:50%:d05-L1:Ctrl:d05 NA NA NA NA
## Lx:50%:d05-L1:Ctrl:d05 NA NA NA NA
## L1:Ctrl:d10-L1:Ctrl:d05 NA NA NA NA
## Lx:Ctrl:d10-L1:Ctrl:d05 NA NA NA NA
## L1:05%:d10-L1:Ctrl:d05 NA NA NA NA
## Lx:05%:d10-L1:Ctrl:d05 NA NA NA NA
## L1:10%:d10-L1:Ctrl:d05 NA NA NA NA
## Lx:10%:d10-L1:Ctrl:d05 NA NA NA NA
## L1:25%:d10-L1:Ctrl:d05 NA NA NA NA
## Lx:25%:d10-L1:Ctrl:d05 NA NA NA NA
## L1:50%:d10-L1:Ctrl:d05 NA NA NA NA
## Lx:50%:d10-L1:Ctrl:d05 NA NA NA NA
## L1:Ctrl:d15-L1:Ctrl:d05 NA NA NA NA
## Lx:Ctrl:d15-L1:Ctrl:d05 NA NA NA NA
## L1:05%:d15-L1:Ctrl:d05 NA NA NA NA
## Lx:05%:d15-L1:Ctrl:d05 NA NA NA NA
## L1:10%:d15-L1:Ctrl:d05 NA NA NA NA
## Lx:10%:d15-L1:Ctrl:d05 NA NA NA NA
## L1:25%:d15-L1:Ctrl:d05 NA NA NA NA
## Lx:25%:d15-L1:Ctrl:d05 NA NA NA NA
## L1:50%:d15-L1:Ctrl:d05 NA NA NA NA
## Lx:50%:d15-L1:Ctrl:d05 NA NA NA NA
## L1:05%:d05-Lx:Ctrl:d05 NA NA NA NA
## Lx:05%:d05-Lx:Ctrl:d05 NA NA NA NA
## L1:10%:d05-Lx:Ctrl:d05 NA NA NA NA
## Lx:10%:d05-Lx:Ctrl:d05 NA NA NA NA
## L1:25%:d05-Lx:Ctrl:d05 NA NA NA NA
## Lx:25%:d05-Lx:Ctrl:d05 NA NA NA NA
## L1:50%:d05-Lx:Ctrl:d05 NA NA NA NA
## Lx:50%:d05-Lx:Ctrl:d05 NA NA NA NA
## L1:Ctrl:d10-Lx:Ctrl:d05 NA NA NA NA
## Lx:Ctrl:d10-Lx:Ctrl:d05 NA NA NA NA
## L1:05%:d10-Lx:Ctrl:d05 NA NA NA NA
## Lx:05%:d10-Lx:Ctrl:d05 NA NA NA NA
## L1:10%:d10-Lx:Ctrl:d05 NA NA NA NA
## Lx:10%:d10-Lx:Ctrl:d05 NA NA NA NA
## L1:25%:d10-Lx:Ctrl:d05 NA NA NA NA
## Lx:25%:d10-Lx:Ctrl:d05 NA NA NA NA
## L1:50%:d10-Lx:Ctrl:d05 NA NA NA NA
## Lx:50%:d10-Lx:Ctrl:d05 NA NA NA NA
## L1:Ctrl:d15-Lx:Ctrl:d05 NA NA NA NA
## Lx:Ctrl:d15-Lx:Ctrl:d05 NA NA NA NA
## L1:05%:d15-Lx:Ctrl:d05 NA NA NA NA
## Lx:05%:d15-Lx:Ctrl:d05 NA NA NA NA
## L1:10%:d15-Lx:Ctrl:d05 NA NA NA NA
## Lx:10%:d15-Lx:Ctrl:d05 NA NA NA NA
## L1:25%:d15-Lx:Ctrl:d05 NA NA NA NA
## Lx:25%:d15-Lx:Ctrl:d05 NA NA NA NA
## L1:50%:d15-Lx:Ctrl:d05 NA NA NA NA
## Lx:50%:d15-Lx:Ctrl:d05 NA NA NA NA
## Lx:05%:d05-L1:05%:d05 3.000000e+00 -3.94762466 9.9476247 0.9995830
## L1:10%:d05-L1:05%:d05 -1.776357e-15 -6.94762466 6.9476247 1.0000000
## Lx:10%:d05-L1:05%:d05 2.333333e+00 -4.61429133 9.2809580 0.9999993
## L1:25%:d05-L1:05%:d05 -1.776357e-15 -6.94762466 6.9476247 1.0000000
## Lx:25%:d05-L1:05%:d05 1.333333e+00 -5.61429133 8.2809580 1.0000000
## L1:50%:d05-L1:05%:d05 5.666667e+00 -2.10101385 13.4343472 0.5818083
## Lx:50%:d05-L1:05%:d05 -3.333333e-01 -7.28095800 6.6142913 1.0000000
## L1:Ctrl:d10-L1:05%:d05 NA NA NA NA
## Lx:Ctrl:d10-L1:05%:d05 NA NA NA NA
## L1:05%:d10-L1:05%:d05 6.666667e-01 -6.28095800 7.6142913 1.0000000
## Lx:05%:d10-L1:05%:d05 2.333333e+00 -4.61429133 9.2809580 0.9999993
## L1:10%:d10-L1:05%:d05 -3.333333e-01 -7.28095800 6.6142913 1.0000000
## Lx:10%:d10-L1:05%:d05 3.000000e+00 -3.94762466 9.9476247 0.9995830
## L1:25%:d10-L1:05%:d05 -3.333333e-01 -7.28095800 6.6142913 1.0000000
## Lx:25%:d10-L1:05%:d05 2.000000e+00 -4.94762466 8.9476247 1.0000000
## L1:50%:d10-L1:05%:d05 -1.666667e+00 -8.61429133 5.2809580 1.0000000
## Lx:50%:d10-L1:05%:d05 3.666667e+00 -3.28095800 10.6142913 0.9838739
## L1:Ctrl:d15-L1:05%:d05 NA NA NA NA
## Lx:Ctrl:d15-L1:05%:d05 NA NA NA NA
## L1:05%:d15-L1:05%:d05 1.333333e+00 -5.61429133 8.2809580 1.0000000
## Lx:05%:d15-L1:05%:d05 2.333333e+00 -4.61429133 9.2809580 0.9999993
## L1:10%:d15-L1:05%:d05 6.666667e-01 -6.28095800 7.6142913 1.0000000
## Lx:10%:d15-L1:05%:d05 2.333333e+00 -4.61429133 9.2809580 0.9999993
## L1:25%:d15-L1:05%:d05 1.333333e+00 -5.61429133 8.2809580 1.0000000
## Lx:25%:d15-L1:05%:d05 2.333333e+00 -4.61429133 9.2809580 0.9999993
## L1:50%:d15-L1:05%:d05 2.333333e+00 -4.61429133 9.2809580 0.9999993
## Lx:50%:d15-L1:05%:d05 5.666667e+00 -1.28095800 12.6142913 0.3253849
## L1:10%:d05-Lx:05%:d05 -3.000000e+00 -9.94762466 3.9476247 0.9995830
## Lx:10%:d05-Lx:05%:d05 -6.666667e-01 -7.61429133 6.2809580 1.0000000
## L1:25%:d05-Lx:05%:d05 -3.000000e+00 -9.94762466 3.9476247 0.9995830
## Lx:25%:d05-Lx:05%:d05 -1.666667e+00 -8.61429133 5.2809580 1.0000000
## L1:50%:d05-Lx:05%:d05 2.666667e+00 -5.10101385 10.4343472 0.9999986
## Lx:50%:d05-Lx:05%:d05 -3.333333e+00 -10.28095800 3.6142913 0.9966607
## L1:Ctrl:d10-Lx:05%:d05 NA NA NA NA
## Lx:Ctrl:d10-Lx:05%:d05 NA NA NA NA
## L1:05%:d10-Lx:05%:d05 -2.333333e+00 -9.28095800 4.6142913 0.9999993
## Lx:05%:d10-Lx:05%:d05 -6.666667e-01 -7.61429133 6.2809580 1.0000000
## L1:10%:d10-Lx:05%:d05 -3.333333e+00 -10.28095800 3.6142913 0.9966607
## Lx:10%:d10-Lx:05%:d05 2.664535e-15 -6.94762466 6.9476247 1.0000000
## L1:25%:d10-Lx:05%:d05 -3.333333e+00 -10.28095800 3.6142913 0.9966607
## Lx:25%:d10-Lx:05%:d05 -1.000000e+00 -7.94762466 5.9476247 1.0000000
## L1:50%:d10-Lx:05%:d05 -4.666667e+00 -11.61429133 2.2809580 0.7572674
## Lx:50%:d10-Lx:05%:d05 6.666667e-01 -6.28095800 7.6142913 1.0000000
## L1:Ctrl:d15-Lx:05%:d05 NA NA NA NA
## Lx:Ctrl:d15-Lx:05%:d05 NA NA NA NA
## L1:05%:d15-Lx:05%:d05 -1.666667e+00 -8.61429133 5.2809580 1.0000000
## Lx:05%:d15-Lx:05%:d05 -6.666667e-01 -7.61429133 6.2809580 1.0000000
## L1:10%:d15-Lx:05%:d05 -2.333333e+00 -9.28095800 4.6142913 0.9999993
## Lx:10%:d15-Lx:05%:d05 -6.666667e-01 -7.61429133 6.2809580 1.0000000
## L1:25%:d15-Lx:05%:d05 -1.666667e+00 -8.61429133 5.2809580 1.0000000
## Lx:25%:d15-Lx:05%:d05 -6.666667e-01 -7.61429133 6.2809580 1.0000000
## L1:50%:d15-Lx:05%:d05 -6.666667e-01 -7.61429133 6.2809580 1.0000000
## Lx:50%:d15-Lx:05%:d05 2.666667e+00 -4.28095800 9.6142913 0.9999731
## Lx:10%:d05-L1:10%:d05 2.333333e+00 -4.61429133 9.2809580 0.9999993
## L1:25%:d05-L1:10%:d05 0.000000e+00 -6.94762466 6.9476247 1.0000000
## Lx:25%:d05-L1:10%:d05 1.333333e+00 -5.61429133 8.2809580 1.0000000
## L1:50%:d05-L1:10%:d05 5.666667e+00 -2.10101385 13.4343472 0.5818083
## Lx:50%:d05-L1:10%:d05 -3.333333e-01 -7.28095800 6.6142913 1.0000000
## L1:Ctrl:d10-L1:10%:d05 NA NA NA NA
## Lx:Ctrl:d10-L1:10%:d05 NA NA NA NA
## L1:05%:d10-L1:10%:d05 6.666667e-01 -6.28095800 7.6142913 1.0000000
## Lx:05%:d10-L1:10%:d05 2.333333e+00 -4.61429133 9.2809580 0.9999993
## L1:10%:d10-L1:10%:d05 -3.333333e-01 -7.28095800 6.6142913 1.0000000
## Lx:10%:d10-L1:10%:d05 3.000000e+00 -3.94762466 9.9476247 0.9995830
## L1:25%:d10-L1:10%:d05 -3.333333e-01 -7.28095800 6.6142913 1.0000000
## Lx:25%:d10-L1:10%:d05 2.000000e+00 -4.94762466 8.9476247 1.0000000
## L1:50%:d10-L1:10%:d05 -1.666667e+00 -8.61429133 5.2809580 1.0000000
## Lx:50%:d10-L1:10%:d05 3.666667e+00 -3.28095800 10.6142913 0.9838739
## L1:Ctrl:d15-L1:10%:d05 NA NA NA NA
## Lx:Ctrl:d15-L1:10%:d05 NA NA NA NA
## L1:05%:d15-L1:10%:d05 1.333333e+00 -5.61429133 8.2809580 1.0000000
## Lx:05%:d15-L1:10%:d05 2.333333e+00 -4.61429133 9.2809580 0.9999993
## L1:10%:d15-L1:10%:d05 6.666667e-01 -6.28095800 7.6142913 1.0000000
## Lx:10%:d15-L1:10%:d05 2.333333e+00 -4.61429133 9.2809580 0.9999993
## L1:25%:d15-L1:10%:d05 1.333333e+00 -5.61429133 8.2809580 1.0000000
## Lx:25%:d15-L1:10%:d05 2.333333e+00 -4.61429133 9.2809580 0.9999993
## L1:50%:d15-L1:10%:d05 2.333333e+00 -4.61429133 9.2809580 0.9999993
## Lx:50%:d15-L1:10%:d05 5.666667e+00 -1.28095800 12.6142913 0.3253849
## L1:25%:d05-Lx:10%:d05 -2.333333e+00 -9.28095800 4.6142913 0.9999993
## Lx:25%:d05-Lx:10%:d05 -1.000000e+00 -7.94762466 5.9476247 1.0000000
## L1:50%:d05-Lx:10%:d05 3.333333e+00 -4.43434718 11.1010138 0.9996352
## Lx:50%:d05-Lx:10%:d05 -2.666667e+00 -9.61429133 4.2809580 0.9999731
## L1:Ctrl:d10-Lx:10%:d05 NA NA NA NA
## Lx:Ctrl:d10-Lx:10%:d05 NA NA NA NA
## L1:05%:d10-Lx:10%:d05 -1.666667e+00 -8.61429133 5.2809580 1.0000000
## Lx:05%:d10-Lx:10%:d05 8.881784e-16 -6.94762466 6.9476247 1.0000000
## L1:10%:d10-Lx:10%:d05 -2.666667e+00 -9.61429133 4.2809580 0.9999731
## Lx:10%:d10-Lx:10%:d05 6.666667e-01 -6.28095800 7.6142913 1.0000000
## L1:25%:d10-Lx:10%:d05 -2.666667e+00 -9.61429133 4.2809580 0.9999731
## Lx:25%:d10-Lx:10%:d05 -3.333333e-01 -7.28095800 6.6142913 1.0000000
## L1:50%:d10-Lx:10%:d05 -4.000000e+00 -10.94762466 2.9476247 0.9473193
## Lx:50%:d10-Lx:10%:d05 1.333333e+00 -5.61429133 8.2809580 1.0000000
## L1:Ctrl:d15-Lx:10%:d05 NA NA NA NA
## Lx:Ctrl:d15-Lx:10%:d05 NA NA NA NA
## L1:05%:d15-Lx:10%:d05 -1.000000e+00 -7.94762466 5.9476247 1.0000000
## Lx:05%:d15-Lx:10%:d05 2.664535e-15 -6.94762466 6.9476247 1.0000000
## L1:10%:d15-Lx:10%:d05 -1.666667e+00 -8.61429133 5.2809580 1.0000000
## Lx:10%:d15-Lx:10%:d05 1.776357e-15 -6.94762466 6.9476247 1.0000000
## L1:25%:d15-Lx:10%:d05 -1.000000e+00 -7.94762466 5.9476247 1.0000000
## Lx:25%:d15-Lx:10%:d05 -5.329071e-15 -6.94762466 6.9476247 1.0000000
## L1:50%:d15-Lx:10%:d05 4.440892e-15 -6.94762466 6.9476247 1.0000000
## Lx:50%:d15-Lx:10%:d05 3.333333e+00 -3.61429133 10.2809580 0.9966607
## Lx:25%:d05-L1:25%:d05 1.333333e+00 -5.61429133 8.2809580 1.0000000
## L1:50%:d05-L1:25%:d05 5.666667e+00 -2.10101385 13.4343472 0.5818083
## Lx:50%:d05-L1:25%:d05 -3.333333e-01 -7.28095800 6.6142913 1.0000000
## L1:Ctrl:d10-L1:25%:d05 NA NA NA NA
## Lx:Ctrl:d10-L1:25%:d05 NA NA NA NA
## L1:05%:d10-L1:25%:d05 6.666667e-01 -6.28095800 7.6142913 1.0000000
## Lx:05%:d10-L1:25%:d05 2.333333e+00 -4.61429133 9.2809580 0.9999993
## L1:10%:d10-L1:25%:d05 -3.333333e-01 -7.28095800 6.6142913 1.0000000
## Lx:10%:d10-L1:25%:d05 3.000000e+00 -3.94762466 9.9476247 0.9995830
## L1:25%:d10-L1:25%:d05 -3.333333e-01 -7.28095800 6.6142913 1.0000000
## Lx:25%:d10-L1:25%:d05 2.000000e+00 -4.94762466 8.9476247 1.0000000
## L1:50%:d10-L1:25%:d05 -1.666667e+00 -8.61429133 5.2809580 1.0000000
## Lx:50%:d10-L1:25%:d05 3.666667e+00 -3.28095800 10.6142913 0.9838739
## L1:Ctrl:d15-L1:25%:d05 NA NA NA NA
## Lx:Ctrl:d15-L1:25%:d05 NA NA NA NA
## L1:05%:d15-L1:25%:d05 1.333333e+00 -5.61429133 8.2809580 1.0000000
## Lx:05%:d15-L1:25%:d05 2.333333e+00 -4.61429133 9.2809580 0.9999993
## L1:10%:d15-L1:25%:d05 6.666667e-01 -6.28095800 7.6142913 1.0000000
## Lx:10%:d15-L1:25%:d05 2.333333e+00 -4.61429133 9.2809580 0.9999993
## L1:25%:d15-L1:25%:d05 1.333333e+00 -5.61429133 8.2809580 1.0000000
## Lx:25%:d15-L1:25%:d05 2.333333e+00 -4.61429133 9.2809580 0.9999993
## L1:50%:d15-L1:25%:d05 2.333333e+00 -4.61429133 9.2809580 0.9999993
## Lx:50%:d15-L1:25%:d05 5.666667e+00 -1.28095800 12.6142913 0.3253849
## L1:50%:d05-Lx:25%:d05 4.333333e+00 -3.43434718 12.1010138 0.9647329
## Lx:50%:d05-Lx:25%:d05 -1.666667e+00 -8.61429133 5.2809580 1.0000000
## L1:Ctrl:d10-Lx:25%:d05 NA NA NA NA
## Lx:Ctrl:d10-Lx:25%:d05 NA NA NA NA
## L1:05%:d10-Lx:25%:d05 -6.666667e-01 -7.61429133 6.2809580 1.0000000
## Lx:05%:d10-Lx:25%:d05 1.000000e+00 -5.94762466 7.9476247 1.0000000
## L1:10%:d10-Lx:25%:d05 -1.666667e+00 -8.61429133 5.2809580 1.0000000
## Lx:10%:d10-Lx:25%:d05 1.666667e+00 -5.28095800 8.6142913 1.0000000
## L1:25%:d10-Lx:25%:d05 -1.666667e+00 -8.61429133 5.2809580 1.0000000
## Lx:25%:d10-Lx:25%:d05 6.666667e-01 -6.28095800 7.6142913 1.0000000
## L1:50%:d10-Lx:25%:d05 -3.000000e+00 -9.94762466 3.9476247 0.9995830
## Lx:50%:d10-Lx:25%:d05 2.333333e+00 -4.61429133 9.2809580 0.9999993
## L1:Ctrl:d15-Lx:25%:d05 NA NA NA NA
## Lx:Ctrl:d15-Lx:25%:d05 NA NA NA NA
## L1:05%:d15-Lx:25%:d05 -3.552714e-15 -6.94762466 6.9476247 1.0000000
## Lx:05%:d15-Lx:25%:d05 1.000000e+00 -5.94762466 7.9476247 1.0000000
## L1:10%:d15-Lx:25%:d05 -6.666667e-01 -7.61429133 6.2809580 1.0000000
## Lx:10%:d15-Lx:25%:d05 1.000000e+00 -5.94762466 7.9476247 1.0000000
## L1:25%:d15-Lx:25%:d05 -7.993606e-15 -6.94762466 6.9476247 1.0000000
## Lx:25%:d15-Lx:25%:d05 1.000000e+00 -5.94762466 7.9476247 1.0000000
## L1:50%:d15-Lx:25%:d05 1.000000e+00 -5.94762466 7.9476247 1.0000000
## Lx:50%:d15-Lx:25%:d05 4.333333e+00 -2.61429133 11.2809580 0.8728649
## Lx:50%:d05-L1:50%:d05 -6.000000e+00 -13.76768052 1.7676805 0.4475651
## L1:Ctrl:d10-L1:50%:d05 NA NA NA NA
## Lx:Ctrl:d10-L1:50%:d05 NA NA NA NA
## L1:05%:d10-L1:50%:d05 -5.000000e+00 -12.76768052 2.7676805 0.8293475
## Lx:05%:d10-L1:50%:d05 -3.333333e+00 -11.10101385 4.4343472 0.9996352
## L1:10%:d10-L1:50%:d05 -6.000000e+00 -13.76768052 1.7676805 0.4475651
## Lx:10%:d10-L1:50%:d05 -2.666667e+00 -10.43434718 5.1010138 0.9999986
## L1:25%:d10-L1:50%:d05 -6.000000e+00 -13.76768052 1.7676805 0.4475651
## Lx:25%:d10-L1:50%:d05 -3.666667e+00 -11.43434718 4.1010138 0.9975210
## L1:50%:d10-L1:50%:d05 -7.333333e+00 -15.10101385 0.4343472 0.0951649
## Lx:50%:d10-L1:50%:d05 -2.000000e+00 -9.76768052 5.7676805 1.0000000
## L1:Ctrl:d15-L1:50%:d05 NA NA NA NA
## Lx:Ctrl:d15-L1:50%:d05 NA NA NA NA
## L1:05%:d15-L1:50%:d05 -4.333333e+00 -12.10101385 3.4343472 0.9647329
## Lx:05%:d15-L1:50%:d05 -3.333333e+00 -11.10101385 4.4343472 0.9996352
## L1:10%:d15-L1:50%:d05 -5.000000e+00 -12.76768052 2.7676805 0.8293475
## Lx:10%:d15-L1:50%:d05 -3.333333e+00 -11.10101385 4.4343472 0.9996352
## L1:25%:d15-L1:50%:d05 -4.333333e+00 -12.10101385 3.4343472 0.9647329
## Lx:25%:d15-L1:50%:d05 -3.333333e+00 -11.10101385 4.4343472 0.9996352
## L1:50%:d15-L1:50%:d05 -3.333333e+00 -11.10101385 4.4343472 0.9996352
## Lx:50%:d15-L1:50%:d05 7.105427e-15 -7.76768052 7.7676805 1.0000000
## L1:Ctrl:d10-Lx:50%:d05 NA NA NA NA
## Lx:Ctrl:d10-Lx:50%:d05 NA NA NA NA
## L1:05%:d10-Lx:50%:d05 1.000000e+00 -5.94762466 7.9476247 1.0000000
## Lx:05%:d10-Lx:50%:d05 2.666667e+00 -4.28095800 9.6142913 0.9999731
## L1:10%:d10-Lx:50%:d05 1.332268e-15 -6.94762466 6.9476247 1.0000000
## Lx:10%:d10-Lx:50%:d05 3.333333e+00 -3.61429133 10.2809580 0.9966607
## L1:25%:d10-Lx:50%:d05 3.552714e-15 -6.94762466 6.9476247 1.0000000
## Lx:25%:d10-Lx:50%:d05 2.333333e+00 -4.61429133 9.2809580 0.9999993
## L1:50%:d10-Lx:50%:d05 -1.333333e+00 -8.28095800 5.6142913 1.0000000
## Lx:50%:d10-Lx:50%:d05 4.000000e+00 -2.94762466 10.9476247 0.9473193
## L1:Ctrl:d15-Lx:50%:d05 NA NA NA NA
## Lx:Ctrl:d15-Lx:50%:d05 NA NA NA NA
## L1:05%:d15-Lx:50%:d05 1.666667e+00 -5.28095800 8.6142913 1.0000000
## Lx:05%:d15-Lx:50%:d05 2.666667e+00 -4.28095800 9.6142913 0.9999731
## L1:10%:d15-Lx:50%:d05 1.000000e+00 -5.94762466 7.9476247 1.0000000
## Lx:10%:d15-Lx:50%:d05 2.666667e+00 -4.28095800 9.6142913 0.9999731
## L1:25%:d15-Lx:50%:d05 1.666667e+00 -5.28095800 8.6142913 1.0000000
## Lx:25%:d15-Lx:50%:d05 2.666667e+00 -4.28095800 9.6142913 0.9999731
## L1:50%:d15-Lx:50%:d05 2.666667e+00 -4.28095800 9.6142913 0.9999731
## Lx:50%:d15-Lx:50%:d05 6.000000e+00 -0.94762466 12.9476247 0.2152019
## Lx:Ctrl:d10-L1:Ctrl:d10 NA NA NA NA
## L1:05%:d10-L1:Ctrl:d10 NA NA NA NA
## Lx:05%:d10-L1:Ctrl:d10 NA NA NA NA
## L1:10%:d10-L1:Ctrl:d10 NA NA NA NA
## Lx:10%:d10-L1:Ctrl:d10 NA NA NA NA
## L1:25%:d10-L1:Ctrl:d10 NA NA NA NA
## Lx:25%:d10-L1:Ctrl:d10 NA NA NA NA
## L1:50%:d10-L1:Ctrl:d10 NA NA NA NA
## Lx:50%:d10-L1:Ctrl:d10 NA NA NA NA
## L1:Ctrl:d15-L1:Ctrl:d10 NA NA NA NA
## Lx:Ctrl:d15-L1:Ctrl:d10 NA NA NA NA
## L1:05%:d15-L1:Ctrl:d10 NA NA NA NA
## Lx:05%:d15-L1:Ctrl:d10 NA NA NA NA
## L1:10%:d15-L1:Ctrl:d10 NA NA NA NA
## Lx:10%:d15-L1:Ctrl:d10 NA NA NA NA
## L1:25%:d15-L1:Ctrl:d10 NA NA NA NA
## Lx:25%:d15-L1:Ctrl:d10 NA NA NA NA
## L1:50%:d15-L1:Ctrl:d10 NA NA NA NA
## Lx:50%:d15-L1:Ctrl:d10 NA NA NA NA
## L1:05%:d10-Lx:Ctrl:d10 NA NA NA NA
## Lx:05%:d10-Lx:Ctrl:d10 NA NA NA NA
## L1:10%:d10-Lx:Ctrl:d10 NA NA NA NA
## Lx:10%:d10-Lx:Ctrl:d10 NA NA NA NA
## L1:25%:d10-Lx:Ctrl:d10 NA NA NA NA
## Lx:25%:d10-Lx:Ctrl:d10 NA NA NA NA
## L1:50%:d10-Lx:Ctrl:d10 NA NA NA NA
## Lx:50%:d10-Lx:Ctrl:d10 NA NA NA NA
## L1:Ctrl:d15-Lx:Ctrl:d10 NA NA NA NA
## Lx:Ctrl:d15-Lx:Ctrl:d10 NA NA NA NA
## L1:05%:d15-Lx:Ctrl:d10 NA NA NA NA
## Lx:05%:d15-Lx:Ctrl:d10 NA NA NA NA
## L1:10%:d15-Lx:Ctrl:d10 NA NA NA NA
## Lx:10%:d15-Lx:Ctrl:d10 NA NA NA NA
## L1:25%:d15-Lx:Ctrl:d10 NA NA NA NA
## Lx:25%:d15-Lx:Ctrl:d10 NA NA NA NA
## L1:50%:d15-Lx:Ctrl:d10 NA NA NA NA
## Lx:50%:d15-Lx:Ctrl:d10 NA NA NA NA
## Lx:05%:d10-L1:05%:d10 1.666667e+00 -5.28095800 8.6142913 1.0000000
## L1:10%:d10-L1:05%:d10 -1.000000e+00 -7.94762466 5.9476247 1.0000000
## Lx:10%:d10-L1:05%:d10 2.333333e+00 -4.61429133 9.2809580 0.9999993
## L1:25%:d10-L1:05%:d10 -1.000000e+00 -7.94762466 5.9476247 1.0000000
## Lx:25%:d10-L1:05%:d10 1.333333e+00 -5.61429133 8.2809580 1.0000000
## L1:50%:d10-L1:05%:d10 -2.333333e+00 -9.28095800 4.6142913 0.9999993
## Lx:50%:d10-L1:05%:d10 3.000000e+00 -3.94762466 9.9476247 0.9995830
## L1:Ctrl:d15-L1:05%:d10 NA NA NA NA
## Lx:Ctrl:d15-L1:05%:d10 NA NA NA NA
## L1:05%:d15-L1:05%:d10 6.666667e-01 -6.28095800 7.6142913 1.0000000
## Lx:05%:d15-L1:05%:d10 1.666667e+00 -5.28095800 8.6142913 1.0000000
## L1:10%:d15-L1:05%:d10 -3.552714e-15 -6.94762466 6.9476247 1.0000000
## Lx:10%:d15-L1:05%:d10 1.666667e+00 -5.28095800 8.6142913 1.0000000
## L1:25%:d15-L1:05%:d10 6.666667e-01 -6.28095800 7.6142913 1.0000000
## Lx:25%:d15-L1:05%:d10 1.666667e+00 -5.28095800 8.6142913 1.0000000
## L1:50%:d15-L1:05%:d10 1.666667e+00 -5.28095800 8.6142913 1.0000000
## Lx:50%:d15-L1:05%:d10 5.000000e+00 -1.94762466 11.9476247 0.6130052
## L1:10%:d10-Lx:05%:d10 -2.666667e+00 -9.61429133 4.2809580 0.9999731
## Lx:10%:d10-Lx:05%:d10 6.666667e-01 -6.28095800 7.6142913 1.0000000
## L1:25%:d10-Lx:05%:d10 -2.666667e+00 -9.61429133 4.2809580 0.9999731
## Lx:25%:d10-Lx:05%:d10 -3.333333e-01 -7.28095800 6.6142913 1.0000000
## L1:50%:d10-Lx:05%:d10 -4.000000e+00 -10.94762466 2.9476247 0.9473193
## Lx:50%:d10-Lx:05%:d10 1.333333e+00 -5.61429133 8.2809580 1.0000000
## L1:Ctrl:d15-Lx:05%:d10 NA NA NA NA
## Lx:Ctrl:d15-Lx:05%:d10 NA NA NA NA
## L1:05%:d15-Lx:05%:d10 -1.000000e+00 -7.94762466 5.9476247 1.0000000
## Lx:05%:d15-Lx:05%:d10 1.776357e-15 -6.94762466 6.9476247 1.0000000
## L1:10%:d15-Lx:05%:d10 -1.666667e+00 -8.61429133 5.2809580 1.0000000
## Lx:10%:d15-Lx:05%:d10 8.881784e-16 -6.94762466 6.9476247 1.0000000
## L1:25%:d15-Lx:05%:d10 -1.000000e+00 -7.94762466 5.9476247 1.0000000
## Lx:25%:d15-Lx:05%:d10 -6.217249e-15 -6.94762466 6.9476247 1.0000000
## L1:50%:d15-Lx:05%:d10 3.552714e-15 -6.94762466 6.9476247 1.0000000
## Lx:50%:d15-Lx:05%:d10 3.333333e+00 -3.61429133 10.2809580 0.9966607
## Lx:10%:d10-L1:10%:d10 3.333333e+00 -3.61429133 10.2809580 0.9966607
## L1:25%:d10-L1:10%:d10 2.220446e-15 -6.94762466 6.9476247 1.0000000
## Lx:25%:d10-L1:10%:d10 2.333333e+00 -4.61429133 9.2809580 0.9999993
## L1:50%:d10-L1:10%:d10 -1.333333e+00 -8.28095800 5.6142913 1.0000000
## Lx:50%:d10-L1:10%:d10 4.000000e+00 -2.94762466 10.9476247 0.9473193
## L1:Ctrl:d15-L1:10%:d10 NA NA NA NA
## Lx:Ctrl:d15-L1:10%:d10 NA NA NA NA
## L1:05%:d15-L1:10%:d10 1.666667e+00 -5.28095800 8.6142913 1.0000000
## Lx:05%:d15-L1:10%:d10 2.666667e+00 -4.28095800 9.6142913 0.9999731
## L1:10%:d15-L1:10%:d10 1.000000e+00 -5.94762466 7.9476247 1.0000000
## Lx:10%:d15-L1:10%:d10 2.666667e+00 -4.28095800 9.6142913 0.9999731
## L1:25%:d15-L1:10%:d10 1.666667e+00 -5.28095800 8.6142913 1.0000000
## Lx:25%:d15-L1:10%:d10 2.666667e+00 -4.28095800 9.6142913 0.9999731
## L1:50%:d15-L1:10%:d10 2.666667e+00 -4.28095800 9.6142913 0.9999731
## Lx:50%:d15-L1:10%:d10 6.000000e+00 -0.94762466 12.9476247 0.2152019
## L1:25%:d10-Lx:10%:d10 -3.333333e+00 -10.28095800 3.6142913 0.9966607
## Lx:25%:d10-Lx:10%:d10 -1.000000e+00 -7.94762466 5.9476247 1.0000000
## L1:50%:d10-Lx:10%:d10 -4.666667e+00 -11.61429133 2.2809580 0.7572674
## Lx:50%:d10-Lx:10%:d10 6.666667e-01 -6.28095800 7.6142913 1.0000000
## L1:Ctrl:d15-Lx:10%:d10 NA NA NA NA
## Lx:Ctrl:d15-Lx:10%:d10 NA NA NA NA
## L1:05%:d15-Lx:10%:d10 -1.666667e+00 -8.61429133 5.2809580 1.0000000
## Lx:05%:d15-Lx:10%:d10 -6.666667e-01 -7.61429133 6.2809580 1.0000000
## L1:10%:d15-Lx:10%:d10 -2.333333e+00 -9.28095800 4.6142913 0.9999993
## Lx:10%:d15-Lx:10%:d10 -6.666667e-01 -7.61429133 6.2809580 1.0000000
## L1:25%:d15-Lx:10%:d10 -1.666667e+00 -8.61429133 5.2809580 1.0000000
## Lx:25%:d15-Lx:10%:d10 -6.666667e-01 -7.61429133 6.2809580 1.0000000
## L1:50%:d15-Lx:10%:d10 -6.666667e-01 -7.61429133 6.2809580 1.0000000
## Lx:50%:d15-Lx:10%:d10 2.666667e+00 -4.28095800 9.6142913 0.9999731
## Lx:25%:d10-L1:25%:d10 2.333333e+00 -4.61429133 9.2809580 0.9999993
## L1:50%:d10-L1:25%:d10 -1.333333e+00 -8.28095800 5.6142913 1.0000000
## Lx:50%:d10-L1:25%:d10 4.000000e+00 -2.94762466 10.9476247 0.9473193
## L1:Ctrl:d15-L1:25%:d10 NA NA NA NA
## Lx:Ctrl:d15-L1:25%:d10 NA NA NA NA
## L1:05%:d15-L1:25%:d10 1.666667e+00 -5.28095800 8.6142913 1.0000000
## Lx:05%:d15-L1:25%:d10 2.666667e+00 -4.28095800 9.6142913 0.9999731
## L1:10%:d15-L1:25%:d10 1.000000e+00 -5.94762466 7.9476247 1.0000000
## Lx:10%:d15-L1:25%:d10 2.666667e+00 -4.28095800 9.6142913 0.9999731
## L1:25%:d15-L1:25%:d10 1.666667e+00 -5.28095800 8.6142913 1.0000000
## Lx:25%:d15-L1:25%:d10 2.666667e+00 -4.28095800 9.6142913 0.9999731
## L1:50%:d15-L1:25%:d10 2.666667e+00 -4.28095800 9.6142913 0.9999731
## Lx:50%:d15-L1:25%:d10 6.000000e+00 -0.94762466 12.9476247 0.2152019
## L1:50%:d10-Lx:25%:d10 -3.666667e+00 -10.61429133 3.2809580 0.9838739
## Lx:50%:d10-Lx:25%:d10 1.666667e+00 -5.28095800 8.6142913 1.0000000
## L1:Ctrl:d15-Lx:25%:d10 NA NA NA NA
## Lx:Ctrl:d15-Lx:25%:d10 NA NA NA NA
## L1:05%:d15-Lx:25%:d10 -6.666667e-01 -7.61429133 6.2809580 1.0000000
## Lx:05%:d15-Lx:25%:d10 3.333333e-01 -6.61429133 7.2809580 1.0000000
## L1:10%:d15-Lx:25%:d10 -1.333333e+00 -8.28095800 5.6142913 1.0000000
## Lx:10%:d15-Lx:25%:d10 3.333333e-01 -6.61429133 7.2809580 1.0000000
## L1:25%:d15-Lx:25%:d10 -6.666667e-01 -7.61429133 6.2809580 1.0000000
## Lx:25%:d15-Lx:25%:d10 3.333333e-01 -6.61429133 7.2809580 1.0000000
## L1:50%:d15-Lx:25%:d10 3.333333e-01 -6.61429133 7.2809580 1.0000000
## Lx:50%:d15-Lx:25%:d10 3.666667e+00 -3.28095800 10.6142913 0.9838739
## Lx:50%:d10-L1:50%:d10 5.333333e+00 -1.61429133 12.2809580 0.4621129
## L1:Ctrl:d15-L1:50%:d10 NA NA NA NA
## Lx:Ctrl:d15-L1:50%:d10 NA NA NA NA
## L1:05%:d15-L1:50%:d10 3.000000e+00 -3.94762466 9.9476247 0.9995830
## Lx:05%:d15-L1:50%:d10 4.000000e+00 -2.94762466 10.9476247 0.9473193
## L1:10%:d15-L1:50%:d10 2.333333e+00 -4.61429133 9.2809580 0.9999993
## Lx:10%:d15-L1:50%:d10 4.000000e+00 -2.94762466 10.9476247 0.9473193
## L1:25%:d15-L1:50%:d10 3.000000e+00 -3.94762466 9.9476247 0.9995830
## Lx:25%:d15-L1:50%:d10 4.000000e+00 -2.94762466 10.9476247 0.9473193
## L1:50%:d15-L1:50%:d10 4.000000e+00 -2.94762466 10.9476247 0.9473193
## Lx:50%:d15-L1:50%:d10 7.333333e+00 0.38570867 14.2809580 0.0250595
## L1:Ctrl:d15-Lx:50%:d10 NA NA NA NA
## Lx:Ctrl:d15-Lx:50%:d10 NA NA NA NA
## L1:05%:d15-Lx:50%:d10 -2.333333e+00 -9.28095800 4.6142913 0.9999993
## Lx:05%:d15-Lx:50%:d10 -1.333333e+00 -8.28095800 5.6142913 1.0000000
## L1:10%:d15-Lx:50%:d10 -3.000000e+00 -9.94762466 3.9476247 0.9995830
## Lx:10%:d15-Lx:50%:d10 -1.333333e+00 -8.28095800 5.6142913 1.0000000
## L1:25%:d15-Lx:50%:d10 -2.333333e+00 -9.28095800 4.6142913 0.9999993
## Lx:25%:d15-Lx:50%:d10 -1.333333e+00 -8.28095800 5.6142913 1.0000000
## L1:50%:d15-Lx:50%:d10 -1.333333e+00 -8.28095800 5.6142913 1.0000000
## Lx:50%:d15-Lx:50%:d10 2.000000e+00 -4.94762466 8.9476247 1.0000000
## Lx:Ctrl:d15-L1:Ctrl:d15 NA NA NA NA
## L1:05%:d15-L1:Ctrl:d15 NA NA NA NA
## Lx:05%:d15-L1:Ctrl:d15 NA NA NA NA
## L1:10%:d15-L1:Ctrl:d15 NA NA NA NA
## Lx:10%:d15-L1:Ctrl:d15 NA NA NA NA
## L1:25%:d15-L1:Ctrl:d15 NA NA NA NA
## Lx:25%:d15-L1:Ctrl:d15 NA NA NA NA
## L1:50%:d15-L1:Ctrl:d15 NA NA NA NA
## Lx:50%:d15-L1:Ctrl:d15 NA NA NA NA
## L1:05%:d15-Lx:Ctrl:d15 NA NA NA NA
## Lx:05%:d15-Lx:Ctrl:d15 NA NA NA NA
## L1:10%:d15-Lx:Ctrl:d15 NA NA NA NA
## Lx:10%:d15-Lx:Ctrl:d15 NA NA NA NA
## L1:25%:d15-Lx:Ctrl:d15 NA NA NA NA
## Lx:25%:d15-Lx:Ctrl:d15 NA NA NA NA
## L1:50%:d15-Lx:Ctrl:d15 NA NA NA NA
## Lx:50%:d15-Lx:Ctrl:d15 NA NA NA NA
## Lx:05%:d15-L1:05%:d15 1.000000e+00 -5.94762466 7.9476247 1.0000000
## L1:10%:d15-L1:05%:d15 -6.666667e-01 -7.61429133 6.2809580 1.0000000
## Lx:10%:d15-L1:05%:d15 1.000000e+00 -5.94762466 7.9476247 1.0000000
## L1:25%:d15-L1:05%:d15 -4.440892e-15 -6.94762466 6.9476247 1.0000000
## Lx:25%:d15-L1:05%:d15 1.000000e+00 -5.94762466 7.9476247 1.0000000
## L1:50%:d15-L1:05%:d15 1.000000e+00 -5.94762466 7.9476247 1.0000000
## Lx:50%:d15-L1:05%:d15 4.333333e+00 -2.61429133 11.2809580 0.8728649
## L1:10%:d15-Lx:05%:d15 -1.666667e+00 -8.61429133 5.2809580 1.0000000
## Lx:10%:d15-Lx:05%:d15 -8.881784e-16 -6.94762466 6.9476247 1.0000000
## L1:25%:d15-Lx:05%:d15 -1.000000e+00 -7.94762466 5.9476247 1.0000000
## Lx:25%:d15-Lx:05%:d15 -7.993606e-15 -6.94762466 6.9476247 1.0000000
## L1:50%:d15-Lx:05%:d15 1.776357e-15 -6.94762466 6.9476247 1.0000000
## Lx:50%:d15-Lx:05%:d15 3.333333e+00 -3.61429133 10.2809580 0.9966607
## Lx:10%:d15-L1:10%:d15 1.666667e+00 -5.28095800 8.6142913 1.0000000
## L1:25%:d15-L1:10%:d15 6.666667e-01 -6.28095800 7.6142913 1.0000000
## Lx:25%:d15-L1:10%:d15 1.666667e+00 -5.28095800 8.6142913 1.0000000
## L1:50%:d15-L1:10%:d15 1.666667e+00 -5.28095800 8.6142913 1.0000000
## Lx:50%:d15-L1:10%:d15 5.000000e+00 -1.94762466 11.9476247 0.6130052
## L1:25%:d15-Lx:10%:d15 -1.000000e+00 -7.94762466 5.9476247 1.0000000
## Lx:25%:d15-Lx:10%:d15 -7.105427e-15 -6.94762466 6.9476247 1.0000000
## L1:50%:d15-Lx:10%:d15 2.664535e-15 -6.94762466 6.9476247 1.0000000
## Lx:50%:d15-Lx:10%:d15 3.333333e+00 -3.61429133 10.2809580 0.9966607
## Lx:25%:d15-L1:25%:d15 1.000000e+00 -5.94762466 7.9476247 1.0000000
## L1:50%:d15-L1:25%:d15 1.000000e+00 -5.94762466 7.9476247 1.0000000
## Lx:50%:d15-L1:25%:d15 4.333333e+00 -2.61429133 11.2809580 0.8728649
## L1:50%:d15-Lx:25%:d15 9.769963e-15 -6.94762466 6.9476247 1.0000000
## Lx:50%:d15-Lx:25%:d15 3.333333e+00 -3.61429133 10.2809580 0.9966607
## Lx:50%:d15-L1:50%:d15 3.333333e+00 -3.61429133 10.2809580 0.99666073.3 Mean duration of LD
# Treatment
aov_Treatment_LD <- aov(Mean_LD_Ind ~ Treatment, data = data[Day == 17 & Ind > 0])
summary(aov_Treatment_LD)## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 4 8.0 2.003 0.363 0.834
## Residuals 94 518.6 5.517plot(aov_Treatment_LD)
aov_Treatment_LDM <- aov(Mean_LD_M ~ Treatment, data = data[Day == 17 & M > 0])
summary(aov_Treatment_LDM)## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 4 10.6 2.641 0.585 0.674
## Residuals 92 415.5 4.516plot(aov_Treatment_LDM)
aov_Treatment_LDF <- aov(Mean_LD_F ~ Treatment, data = data[Day == 17 & F > 0])
summary(aov_Treatment_LDF)## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 4 13.8 3.449 0.503 0.734
## Residuals 93 638.1 6.861plot(aov_Treatment_LDF)
#Time (Bti_application)
aov_Bti_application_LD <- aov(Mean_LD_Ind ~ Bti_application, data = data[Day == 17 & Ind > 0])
summary(aov_Bti_application_LD)## Df Sum Sq Mean Sq F value Pr(>F)
## Bti_application 4 5.4 1.349 0.243 0.913
## Residuals 94 521.2 5.545plot(aov_Bti_application_LD)
aov_Bti_application_LDM <- aov(Mean_LD_M ~ Bti_application, data = data[Day == 17 & M > 0])
summary(aov_Bti_application_LDM)## Df Sum Sq Mean Sq F value Pr(>F)
## Bti_application 4 2.9 0.716 0.156 0.96
## Residuals 92 423.2 4.600plot(aov_Bti_application_LDM)
aov_Bti_application_LDF <- aov(Mean_LD_F ~ Bti_application, data = data[Day == 17 & F > 0])
summary(aov_Bti_application_LDF)## Df Sum Sq Mean Sq F value Pr(>F)
## Bti_application 4 20.8 5.191 0.765 0.551
## Residuals 93 631.1 6.786plot(aov_Bti_application_LDF)
#Larval_stage
aov_Larval_stage_LD <- aov(Mean_LD_Ind ~ Larval_stage, data = data[Day == 17 & Ind > 0])
summary(aov_Larval_stage_LD)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 399.8 399.8 305.8 <2e-16 ***
## Residuals 97 126.8 1.3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_LD)
TukeyHSD(aov_Larval_stage_LD)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Mean_LD_Ind ~ Larval_stage, data = data[Day == 17 & Ind > 0])
##
## $Larval_stage
## diff lwr upr p adj
## Lx-L1 4.020805 3.564435 4.477176 0aov_Larval_stage_LDM <- aov(Mean_LD_M ~ Larval_stage, data = data[Day == 17 & M > 0])
summary(aov_Larval_stage_LDM)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 315 315.02 269.5 <2e-16 ***
## Residuals 95 111 1.17
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_LDM)
TukeyHSD(aov_Larval_stage_LDM)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Mean_LD_M ~ Larval_stage, data = data[Day == 17 & M > 0])
##
## $Larval_stage
## diff lwr upr p adj
## Lx-L1 3.609052 3.17263 4.045474 0aov_Larval_stage_LDF <- aov(Mean_LD_F ~ Larval_stage, data = data[Day == 17 & F > 0])
summary(aov_Larval_stage_LDF)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 457.4 457.4 225.7 <2e-16 ***
## Residuals 96 194.5 2.0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_LDF)
TukeyHSD(aov_Larval_stage_LDF)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Mean_LD_F ~ Larval_stage, data = data[Day == 17 & F > 0])
##
## $Larval_stage
## diff lwr upr p adj
## Lx-L1 4.324247 3.752921 4.895573 0#Treatment x Bti_application
aov_Treatment_appl_LD <- aov(Mean_LD_Ind ~ Treatment * Bti_application, data = data[Day == 17 & Ind > 0])
summary(aov_Treatment_appl_LD)## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 4 8.0 2.003 0.335 0.854
## Bti_application 3 3.9 1.306 0.218 0.884
## Treatment:Bti_application 9 23.8 2.640 0.441 0.909
## Residuals 82 490.9 5.987plot(aov_Treatment_appl_LD) 
aov_Treatment_appl_LDM <- aov(Mean_LD_M ~ Treatment * Bti_application, data = data[Day == 17 & M > 0])
summary(aov_Treatment_appl_LDM)## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 4 10.6 2.641 0.552 0.698
## Bti_application 3 4.1 1.362 0.285 0.836
## Treatment:Bti_application 9 28.7 3.190 0.667 0.736
## Residuals 80 382.7 4.784plot(aov_Treatment_appl_LDM) 
aov_Treatment_appl_LDF <- aov(Mean_LD_F ~ Treatment * Bti_application, data = data[Day == 17 & F > 0])
summary(aov_Treatment_appl_LDF)## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 4 13.8 3.449 0.461 0.764
## Bti_application 3 15.8 5.279 0.706 0.551
## Treatment:Bti_application 9 16.3 1.812 0.242 0.987
## Residuals 81 605.9 7.481plot(aov_Treatment_appl_LDF)
#Larval_stage x Bti_application
aov_Larval_stage_appl_LD <- aov(Mean_LD_Ind ~ Larval_stage * Bti_application, data = data[Day == 17 & Ind > 0])
summary(aov_Larval_stage_appl_LD)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 399.8 399.8 315.239 <2e-16 ***
## Bti_application 4 11.0 2.7 2.168 0.079 .
## Larval_stage:Bti_application 4 3.0 0.7 0.583 0.676
## Residuals 89 112.9 1.3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_appl_LD)
aov_Larval_stage_appl_LDM <- aov(Mean_LD_M ~ Larval_stage * Bti_application, data = data[Day == 17 & M > 0])
summary(aov_Larval_stage_appl_LDM)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 315.02 315.02 260.778 <2e-16 ***
## Bti_application 4 0.49 0.12 0.102 0.981
## Larval_stage:Bti_application 4 5.44 1.36 1.127 0.349
## Residuals 87 105.10 1.21
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_appl_LDM) 
aov_Larval_stage_appl_LDF <- aov(Mean_LD_F ~ Larval_stage * Bti_application, data = data[Day == 17 & F > 0])
summary(aov_Larval_stage_appl_LDF)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 457.4 457.4 251.658 < 2e-16 ***
## Bti_application 4 31.0 7.8 4.268 0.00334 **
## Larval_stage:Bti_application 4 3.6 0.9 0.490 0.74275
## Residuals 88 159.9 1.8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_appl_LDF)
#Larval_stage x Treatment
aov_Larval_stage_Treatment_LD <- aov(Mean_LD_Ind ~ Larval_stage * Treatment, data = data[Day == 17 & Ind > 0])
summary(aov_Larval_stage_Treatment_LD)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 399.8 399.8 300.669 <2e-16 ***
## Treatment 4 4.9 1.2 0.925 0.453
## Larval_stage:Treatment 4 3.6 0.9 0.671 0.614
## Residuals 89 118.3 1.3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_Treatment_LD)
aov_Larval_stage_Treatment_LDM <- aov(Mean_LD_M ~ Larval_stage * Treatment, data = data[Day == 17 & M > 0])
summary(aov_Larval_stage_Treatment_LDM)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 315.02 315.02 271.200 <2e-16 ***
## Treatment 4 5.16 1.29 1.112 0.356
## Larval_stage:Treatment 4 4.81 1.20 1.036 0.393
## Residuals 87 101.06 1.16
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_Treatment_LDM)
aov_Larval_stage_Treatment_LDF <- aov(Mean_LD_F ~ Larval_stage * Treatment, data = data[Day == 17 & F > 0])
summary(aov_Larval_stage_Treatment_LDF)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 457.4 457.4 233.295 <2e-16 ***
## Treatment 4 8.2 2.0 1.043 0.390
## Larval_stage:Treatment 4 13.8 3.5 1.763 0.144
## Residuals 88 172.5 2.0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_Treatment_LDF)
#Larval_stage x Treatment x Bti_application
aov_Larval_stage_Treatment_Bti_application_LD <- aov(Mean_LD_Ind ~ Larval_stage * Treatment * Bti_application, data = data[Day == 17 & Ind > 0])
summary(aov_Larval_stage_Treatment_Bti_application_LD)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 399.8 399.8 300.588 <2e-16 ***
## Treatment 4 4.9 1.2 0.924 0.4553
## Bti_application 3 10.1 3.4 2.537 0.0642 .
## Larval_stage:Treatment 4 2.7 0.7 0.509 0.7290
## Larval_stage:Bti_application 3 1.8 0.6 0.459 0.7118
## Treatment:Bti_application 9 12.1 1.3 1.007 0.4433
## Larval_stage:Treatment:Bti_application 8 7.4 0.9 0.696 0.6936
## Residuals 66 87.8 1.3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_Treatment_Bti_application_LD)
aov_Larval_stage_Treatment_Bti_application_LDM <- aov(Mean_LD_M ~ Larval_stage * Treatment * Bti_application, data = data[Day == 17 & M > 0])
summary(aov_Larval_stage_Treatment_Bti_application_LDM)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 315.02 315.02 290.002 <2e-16 ***
## Treatment 4 5.16 1.29 1.189 0.324
## Bti_application 3 0.41 0.14 0.126 0.945
## Larval_stage:Treatment 4 4.82 1.20 1.109 0.360
## Larval_stage:Bti_application 3 5.20 1.73 1.596 0.199
## Treatment:Bti_application 9 15.85 1.76 1.621 0.128
## Larval_stage:Treatment:Bti_application 8 10.08 1.26 1.159 0.337
## Residuals 64 69.52 1.09
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_Treatment_Bti_application_LDM)## Warning: not plotting observations with leverage one:
## 19
aov_Larval_stage_Treatment_Bti_application_LDF <- aov(Mean_LD_F ~ Larval_stage * Treatment * Bti_application, data = data[Day == 17 & F > 0])
summary(aov_Larval_stage_Treatment_Bti_application_LDF)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 457.4 457.4 246.912 < 2e-16 ***
## Treatment 4 8.2 2.0 1.104 0.36244
## Bti_application 3 28.2 9.4 5.078 0.00321 **
## Larval_stage:Treatment 4 10.0 2.5 1.343 0.26352
## Larval_stage:Bti_application 3 2.0 0.7 0.364 0.77942
## Treatment:Bti_application 9 12.8 1.4 0.766 0.64773
## Larval_stage:Treatment:Bti_application 8 13.0 1.6 0.876 0.54151
## Residuals 65 120.4 1.9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(aov_Larval_stage_Treatment_Bti_application_LDF)
3.4 % of males
selectday = 17 # change to see results over time
# Treatment
aov_Treatment_G <- aov(GR_M ~ Treatment, data = data[Day == selectday & Ind > 0])
summary(aov_Treatment_G)## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 4 2004 501.0 1.895 0.118
## Residuals 94 24853 264.4plot(aov_Treatment_G)
# Time
aov_Time_G <- aov(GR_M ~ Bti_application, data = data[Day == selectday & Ind > 0])
summary(aov_Time_G)## Df Sum Sq Mean Sq F value Pr(>F)
## Bti_application 4 893 223.2 0.808 0.523
## Residuals 94 25964 276.2plot(aov_Time_G)
# Larval_stage
aov_LS_G <- aov(GR_M ~ Larval_stage, data = data[Day == selectday & Ind > 0])
summary(aov_LS_G)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 615 614.8 2.273 0.135
## Residuals 97 26242 270.5plot(aov_LS_G)
# Time x Treatment
aov_TT_G <- aov(GR_M ~ Treatment * Bti_application, data = data[Day == selectday & Ind > 0])
summary(aov_TT_G)## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 4 2004 501.0 2.057 0.0940 .
## Bti_application 3 796 265.4 1.089 0.3583
## Treatment:Bti_application 9 4081 453.4 1.861 0.0695 .
## Residuals 82 19976 243.6
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(aov_TT_G)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = GR_M ~ Treatment * Bti_application, data = data[Day == selectday & Ind > 0])
##
## $Treatment
## diff lwr upr p adj
## 05%-Ctrl -2.693986 -22.565869 17.177898 0.9955729
## 10%-Ctrl 5.609547 -14.262336 25.481430 0.9335801
## 25%-Ctrl -5.921689 -25.793572 13.950194 0.9201697
## 50%-Ctrl 3.331430 -16.822338 23.485198 0.9905369
## 10%-05% 8.303532 -4.264550 20.871615 0.3564689
## 25%-05% -3.227704 -15.795786 9.340379 0.9521799
## 50%-05% 6.025416 -6.983786 19.034617 0.6968679
## 25%-10% -11.531236 -24.099319 1.036847 0.0877475
## 50%-10% -2.278117 -15.287318 10.731085 0.9882287
## 50%-25% 9.253119 -3.756082 22.262321 0.2829399
##
## $Bti_application
## diff lwr upr p adj
## d0-none -5.2154567 -25.369225 14.93831 0.9508802
## d05-none 2.6004654 -17.271418 22.47235 0.9961382
## d10-none 1.3861573 -18.485726 21.25804 0.9996734
## d15-none 0.5769019 -19.294982 20.44879 0.9999901
## d05-d0 7.8159222 -5.193279 20.82512 0.4544078
## d10-d0 6.6016141 -6.407587 19.61082 0.6195552
## d15-d0 5.7923586 -7.216843 18.80156 0.7269036
## d10-d05 -1.2143081 -13.782391 11.35377 0.9988182
## d15-d05 -2.0235636 -14.591646 10.54452 0.9914375
## d15-d10 -0.8092555 -13.377338 11.75883 0.9997623
##
## $`Treatment:Bti_application`
## diff lwr upr p adj
## 05%:none-Ctrl:none NA NA NA NA
## 10%:none-Ctrl:none NA NA NA NA
## 25%:none-Ctrl:none NA NA NA NA
## 50%:none-Ctrl:none NA NA NA NA
## Ctrl:d0-Ctrl:none NA NA NA NA
## 05%:d0-Ctrl:none 1.9781816 -32.142537 36.09890 1.0000000
## 10%:d0-Ctrl:none 6.2580303 -27.862689 40.37875 1.0000000
## 25%:d0-Ctrl:none -17.8303872 -51.951106 16.29033 0.9502376
## 50%:d0-Ctrl:none -20.0006720 -61.789847 21.78850 0.9805266
## Ctrl:d05-Ctrl:none NA NA NA NA
## 05%:d05-Ctrl:none -9.1639113 -43.284630 24.95681 0.9999976
## 10%:d05-Ctrl:none 9.4911642 -24.629555 43.61188 0.9999954
## 25%:d05-Ctrl:none -0.3714226 -34.492141 33.74930 1.0000000
## 50%:d05-Ctrl:none 10.7713338 -23.349385 44.89205 0.9999551
## Ctrl:d10-Ctrl:none NA NA NA NA
## 05%:d10-Ctrl:none -7.9746511 -42.095370 26.14607 0.9999998
## 10%:d10-Ctrl:none 1.4411270 -32.679592 35.56185 1.0000000
## 25%:d10-Ctrl:none 4.0290920 -30.091627 38.14981 1.0000000
## 50%:d10-Ctrl:none 8.3743639 -25.746355 42.49508 0.9999996
## Ctrl:d15-Ctrl:none NA NA NA NA
## 05%:d15-Ctrl:none 4.3844385 -29.736280 38.50516 1.0000000
## 10%:d15-Ctrl:none 5.2478660 -28.872853 39.36858 1.0000000
## 25%:d15-Ctrl:none -9.5140387 -43.634758 24.60668 0.9999952
## 50%:d15-Ctrl:none 2.5146441 -31.606075 36.63536 1.0000000
## 10%:none-05%:none NA NA NA NA
## 25%:none-05%:none NA NA NA NA
## 50%:none-05%:none NA NA NA NA
## Ctrl:d0-05%:none NA NA NA NA
## 05%:d0-05%:none NA NA NA NA
## 10%:d0-05%:none NA NA NA NA
## 25%:d0-05%:none NA NA NA NA
## 50%:d0-05%:none NA NA NA NA
## Ctrl:d05-05%:none NA NA NA NA
## 05%:d05-05%:none NA NA NA NA
## 10%:d05-05%:none NA NA NA NA
## 25%:d05-05%:none NA NA NA NA
## 50%:d05-05%:none NA NA NA NA
## Ctrl:d10-05%:none NA NA NA NA
## 05%:d10-05%:none NA NA NA NA
## 10%:d10-05%:none NA NA NA NA
## 25%:d10-05%:none NA NA NA NA
## 50%:d10-05%:none NA NA NA NA
## Ctrl:d15-05%:none NA NA NA NA
## 05%:d15-05%:none NA NA NA NA
## 10%:d15-05%:none NA NA NA NA
## 25%:d15-05%:none NA NA NA NA
## 50%:d15-05%:none NA NA NA NA
## 25%:none-10%:none NA NA NA NA
## 50%:none-10%:none NA NA NA NA
## Ctrl:d0-10%:none NA NA NA NA
## 05%:d0-10%:none NA NA NA NA
## 10%:d0-10%:none NA NA NA NA
## 25%:d0-10%:none NA NA NA NA
## 50%:d0-10%:none NA NA NA NA
## Ctrl:d05-10%:none NA NA NA NA
## 05%:d05-10%:none NA NA NA NA
## 10%:d05-10%:none NA NA NA NA
## 25%:d05-10%:none NA NA NA NA
## 50%:d05-10%:none NA NA NA NA
## Ctrl:d10-10%:none NA NA NA NA
## 05%:d10-10%:none NA NA NA NA
## 10%:d10-10%:none NA NA NA NA
## 25%:d10-10%:none NA NA NA NA
## 50%:d10-10%:none NA NA NA NA
## Ctrl:d15-10%:none NA NA NA NA
## 05%:d15-10%:none NA NA NA NA
## 10%:d15-10%:none NA NA NA NA
## 25%:d15-10%:none NA NA NA NA
## 50%:d15-10%:none NA NA NA NA
## 50%:none-25%:none NA NA NA NA
## Ctrl:d0-25%:none NA NA NA NA
## 05%:d0-25%:none NA NA NA NA
## 10%:d0-25%:none NA NA NA NA
## 25%:d0-25%:none NA NA NA NA
## 50%:d0-25%:none NA NA NA NA
## Ctrl:d05-25%:none NA NA NA NA
## 05%:d05-25%:none NA NA NA NA
## 10%:d05-25%:none NA NA NA NA
## 25%:d05-25%:none NA NA NA NA
## 50%:d05-25%:none NA NA NA NA
## Ctrl:d10-25%:none NA NA NA NA
## 05%:d10-25%:none NA NA NA NA
## 10%:d10-25%:none NA NA NA NA
## 25%:d10-25%:none NA NA NA NA
## 50%:d10-25%:none NA NA NA NA
## Ctrl:d15-25%:none NA NA NA NA
## 05%:d15-25%:none NA NA NA NA
## 10%:d15-25%:none NA NA NA NA
## 25%:d15-25%:none NA NA NA NA
## 50%:d15-25%:none NA NA NA NA
## Ctrl:d0-50%:none NA NA NA NA
## 05%:d0-50%:none NA NA NA NA
## 10%:d0-50%:none NA NA NA NA
## 25%:d0-50%:none NA NA NA NA
## 50%:d0-50%:none NA NA NA NA
## Ctrl:d05-50%:none NA NA NA NA
## 05%:d05-50%:none NA NA NA NA
## 10%:d05-50%:none NA NA NA NA
## 25%:d05-50%:none NA NA NA NA
## 50%:d05-50%:none NA NA NA NA
## Ctrl:d10-50%:none NA NA NA NA
## 05%:d10-50%:none NA NA NA NA
## 10%:d10-50%:none NA NA NA NA
## 25%:d10-50%:none NA NA NA NA
## 50%:d10-50%:none NA NA NA NA
## Ctrl:d15-50%:none NA NA NA NA
## 05%:d15-50%:none NA NA NA NA
## 10%:d15-50%:none NA NA NA NA
## 25%:d15-50%:none NA NA NA NA
## 50%:d15-50%:none NA NA NA NA
## 05%:d0-Ctrl:d0 NA NA NA NA
## 10%:d0-Ctrl:d0 NA NA NA NA
## 25%:d0-Ctrl:d0 NA NA NA NA
## 50%:d0-Ctrl:d0 NA NA NA NA
## Ctrl:d05-Ctrl:d0 NA NA NA NA
## 05%:d05-Ctrl:d0 NA NA NA NA
## 10%:d05-Ctrl:d0 NA NA NA NA
## 25%:d05-Ctrl:d0 NA NA NA NA
## 50%:d05-Ctrl:d0 NA NA NA NA
## Ctrl:d10-Ctrl:d0 NA NA NA NA
## 05%:d10-Ctrl:d0 NA NA NA NA
## 10%:d10-Ctrl:d0 NA NA NA NA
## 25%:d10-Ctrl:d0 NA NA NA NA
## 50%:d10-Ctrl:d0 NA NA NA NA
## Ctrl:d15-Ctrl:d0 NA NA NA NA
## 05%:d15-Ctrl:d0 NA NA NA NA
## 10%:d15-Ctrl:d0 NA NA NA NA
## 25%:d15-Ctrl:d0 NA NA NA NA
## 50%:d15-Ctrl:d0 NA NA NA NA
## 10%:d0-05%:d0 4.2798487 -29.840870 38.40057 1.0000000
## 25%:d0-05%:d0 -19.8085688 -53.929288 14.31215 0.8721712
## 50%:d0-05%:d0 -21.9788536 -63.768029 19.81032 0.9469760
## Ctrl:d05-05%:d0 NA NA NA NA
## 05%:d05-05%:d0 -11.1420929 -45.262812 22.97863 0.9999198
## 10%:d05-05%:d0 7.5129826 -26.607736 41.63370 1.0000000
## 25%:d05-05%:d0 -2.3496042 -36.470323 31.77111 1.0000000
## 50%:d05-05%:d0 8.7931522 -25.327567 42.91387 0.9999989
## Ctrl:d10-05%:d0 NA NA NA NA
## 05%:d10-05%:d0 -9.9528327 -44.073552 24.16789 0.9999890
## 10%:d10-05%:d0 -0.5370547 -34.657774 33.58366 1.0000000
## 25%:d10-05%:d0 2.0509103 -32.069809 36.17163 1.0000000
## 50%:d10-05%:d0 6.3961823 -27.724537 40.51690 1.0000000
## Ctrl:d15-05%:d0 NA NA NA NA
## 05%:d15-05%:d0 2.4062569 -31.714462 36.52698 1.0000000
## 10%:d15-05%:d0 3.2696844 -30.851035 37.39040 1.0000000
## 25%:d15-05%:d0 -11.4922204 -45.612939 22.62850 0.9998652
## 50%:d15-05%:d0 0.5364624 -33.584256 34.65718 1.0000000
## 25%:d0-10%:d0 -24.0884175 -58.209136 10.03230 0.5640731
## 50%:d0-10%:d0 -26.2587023 -68.047878 15.53047 0.7721318
## Ctrl:d05-10%:d0 NA NA NA NA
## 05%:d05-10%:d0 -15.4219416 -49.542661 18.69878 0.9901538
## 10%:d05-10%:d0 3.2331339 -30.887585 37.35385 1.0000000
## 25%:d05-10%:d0 -6.6294529 -40.750172 27.49127 1.0000000
## 50%:d05-10%:d0 4.5133035 -29.607415 38.63402 1.0000000
## Ctrl:d10-10%:d0 NA NA NA NA
## 05%:d10-10%:d0 -14.2326814 -48.353400 19.88804 0.9965388
## 10%:d10-10%:d0 -4.8169034 -38.937622 29.30382 1.0000000
## 25%:d10-10%:d0 -2.2289384 -36.349657 31.89178 1.0000000
## 50%:d10-10%:d0 2.1163336 -32.004385 36.23705 1.0000000
## Ctrl:d15-10%:d0 NA NA NA NA
## 05%:d15-10%:d0 -1.8735918 -35.994311 32.24713 1.0000000
## 10%:d15-10%:d0 -1.0101643 -35.130883 33.11055 1.0000000
## 25%:d15-10%:d0 -15.7720691 -49.892788 18.34865 0.9870509
## 50%:d15-10%:d0 -3.7433862 -37.864105 30.37733 1.0000000
## 50%:d0-25%:d0 -2.1702848 -43.959460 39.61889 1.0000000
## Ctrl:d05-25%:d0 NA NA NA NA
## 05%:d05-25%:d0 8.6664759 -25.454243 42.78719 0.9999992
## 10%:d05-25%:d0 27.3215514 -6.799168 61.44227 0.3148108
## 25%:d05-25%:d0 17.4589646 -16.661754 51.57968 0.9597642
## 50%:d05-25%:d0 28.6017210 -5.518998 62.72244 0.2360721
## Ctrl:d10-25%:d0 NA NA NA NA
## 05%:d10-25%:d0 9.8557362 -24.264983 43.97646 0.9999908
## 10%:d10-25%:d0 19.2715142 -14.849205 53.39223 0.8981550
## 25%:d10-25%:d0 21.8594792 -12.261240 55.98020 0.7419657
## 50%:d10-25%:d0 26.2047511 -7.915968 60.32547 0.3944443
## Ctrl:d15-25%:d0 NA NA NA NA
## 05%:d15-25%:d0 22.2148257 -11.905893 56.33554 0.7152782
## 10%:d15-25%:d0 23.0782532 -11.042466 57.19897 0.6471267
## 25%:d15-25%:d0 8.3163485 -25.804370 42.43707 0.9999996
## 50%:d15-25%:d0 20.3450313 -13.775688 54.46575 0.8426451
## Ctrl:d05-50%:d0 NA NA NA NA
## 05%:d05-50%:d0 10.8367607 -30.952415 52.62594 0.9999988
## 10%:d05-50%:d0 29.4918362 -12.297339 71.28101 0.5647714
## 25%:d05-50%:d0 19.6292494 -22.159926 61.41842 0.9843301
## 50%:d05-50%:d0 30.7720058 -11.017170 72.56118 0.4789558
## Ctrl:d10-50%:d0 NA NA NA NA
## 05%:d10-50%:d0 12.0260209 -29.763155 53.81520 0.9999914
## 10%:d10-50%:d0 21.4417989 -20.347377 63.23097 0.9586105
## 25%:d10-50%:d0 24.0297640 -17.759412 65.81894 0.8816962
## 50%:d10-50%:d0 28.3750359 -13.414140 70.16421 0.6398331
## Ctrl:d15-50%:d0 NA NA NA NA
## 05%:d15-50%:d0 24.3851105 -17.404065 66.17429 0.8668397
## 10%:d15-50%:d0 25.2485380 -16.540638 67.03771 0.8264641
## 25%:d15-50%:d0 10.4866332 -31.302542 52.27581 0.9999994
## 50%:d15-50%:d0 22.5153161 -19.273859 64.30449 0.9331962
## 05%:d05-Ctrl:d05 NA NA NA NA
## 10%:d05-Ctrl:d05 NA NA NA NA
## 25%:d05-Ctrl:d05 NA NA NA NA
## 50%:d05-Ctrl:d05 NA NA NA NA
## Ctrl:d10-Ctrl:d05 NA NA NA NA
## 05%:d10-Ctrl:d05 NA NA NA NA
## 10%:d10-Ctrl:d05 NA NA NA NA
## 25%:d10-Ctrl:d05 NA NA NA NA
## 50%:d10-Ctrl:d05 NA NA NA NA
## Ctrl:d15-Ctrl:d05 NA NA NA NA
## 05%:d15-Ctrl:d05 NA NA NA NA
## 10%:d15-Ctrl:d05 NA NA NA NA
## 25%:d15-Ctrl:d05 NA NA NA NA
## 50%:d15-Ctrl:d05 NA NA NA NA
## 10%:d05-05%:d05 18.6550755 -15.465643 52.77579 0.9235168
## 25%:d05-05%:d05 8.7924887 -25.328230 42.91321 0.9999989
## 50%:d05-05%:d05 19.9352451 -14.185474 54.05596 0.8655167
## Ctrl:d10-05%:d05 NA NA NA NA
## 05%:d10-05%:d05 1.1892602 -32.931459 35.30998 1.0000000
## 10%:d10-05%:d05 10.6050382 -23.515681 44.72576 0.9999657
## 25%:d10-05%:d05 13.1930032 -20.927716 47.31372 0.9988229
## 50%:d10-05%:d05 17.5382752 -16.582444 51.65899 0.9578527
## Ctrl:d15-05%:d05 NA NA NA NA
## 05%:d15-05%:d05 13.5483498 -20.572369 47.66907 0.9982658
## 10%:d15-05%:d05 14.4117773 -19.708942 48.53250 0.9958993
## 25%:d15-05%:d05 -0.3501275 -34.470846 33.77059 1.0000000
## 50%:d15-05%:d05 11.6785554 -22.442164 45.79927 0.9998243
## 25%:d05-10%:d05 -9.8625868 -43.983306 24.25813 0.9999906
## 50%:d05-10%:d05 1.2801696 -32.840549 35.40089 1.0000000
## Ctrl:d10-10%:d05 NA NA NA NA
## 05%:d10-10%:d05 -17.4658153 -51.586534 16.65490 0.9596017
## 10%:d10-10%:d05 -8.0500373 -42.170756 26.07068 0.9999998
## 25%:d10-10%:d05 -5.4620723 -39.582791 28.65865 1.0000000
## 50%:d10-10%:d05 -1.1168003 -35.237519 33.00392 1.0000000
## Ctrl:d15-10%:d05 NA NA NA NA
## 05%:d15-10%:d05 -5.1067257 -39.227445 29.01399 1.0000000
## 10%:d15-10%:d05 -4.2432982 -38.364017 29.87742 1.0000000
## 25%:d15-10%:d05 -19.0052030 -53.125922 15.11552 0.9096940
## 50%:d15-10%:d05 -6.9765201 -41.097239 27.14420 1.0000000
## 50%:d05-25%:d05 11.1427563 -22.977963 45.26348 0.9999197
## Ctrl:d10-25%:d05 NA NA NA NA
## 05%:d10-25%:d05 -7.6032285 -41.723947 26.51749 0.9999999
## 10%:d10-25%:d05 1.8125495 -32.308169 35.93327 1.0000000
## 25%:d10-25%:d05 4.4005145 -29.720204 38.52123 1.0000000
## 50%:d10-25%:d05 8.7457865 -25.374932 42.86651 0.9999990
## Ctrl:d15-25%:d05 NA NA NA NA
## 05%:d15-25%:d05 4.7558611 -29.364858 38.87658 1.0000000
## 10%:d15-25%:d05 5.6192886 -28.501430 39.74001 1.0000000
## 25%:d15-25%:d05 -9.1426162 -43.263335 24.97810 0.9999977
## 50%:d15-25%:d05 2.8860666 -31.234652 37.00679 1.0000000
## Ctrl:d10-50%:d05 NA NA NA NA
## 05%:d10-50%:d05 -18.7459848 -52.866704 15.37473 0.9200737
## 10%:d10-50%:d05 -9.3302068 -43.450926 24.79051 0.9999967
## 25%:d10-50%:d05 -6.7422418 -40.862961 27.37848 1.0000000
## 50%:d10-50%:d05 -2.3969699 -36.517689 31.72375 1.0000000
## Ctrl:d15-50%:d05 NA NA NA NA
## 05%:d15-50%:d05 -6.3868953 -40.507614 27.73382 1.0000000
## 10%:d15-50%:d05 -5.5234678 -39.644187 28.59725 1.0000000
## 25%:d15-50%:d05 -20.2853725 -54.406091 13.83535 0.8461008
## 50%:d15-50%:d05 -8.2566897 -42.377409 25.86403 0.9999997
## 05%:d10-Ctrl:d10 NA NA NA NA
## 10%:d10-Ctrl:d10 NA NA NA NA
## 25%:d10-Ctrl:d10 NA NA NA NA
## 50%:d10-Ctrl:d10 NA NA NA NA
## Ctrl:d15-Ctrl:d10 NA NA NA NA
## 05%:d15-Ctrl:d10 NA NA NA NA
## 10%:d15-Ctrl:d10 NA NA NA NA
## 25%:d15-Ctrl:d10 NA NA NA NA
## 50%:d15-Ctrl:d10 NA NA NA NA
## 10%:d10-05%:d10 9.4157780 -24.704941 43.53650 0.9999960
## 25%:d10-05%:d10 12.0037430 -22.116976 46.12446 0.9997257
## 50%:d10-05%:d10 16.3490150 -17.771704 50.46973 0.9802726
## Ctrl:d15-05%:d10 NA NA NA NA
## 05%:d15-05%:d10 12.3590896 -21.761629 46.47981 0.9995643
## 10%:d15-05%:d10 13.2225171 -20.898202 47.34324 0.9987835
## 25%:d15-05%:d10 -1.5393877 -35.660107 32.58133 1.0000000
## 50%:d15-05%:d10 10.4892951 -23.631424 44.61001 0.9999717
## 25%:d10-10%:d10 2.5879650 -31.532754 36.70868 1.0000000
## 50%:d10-10%:d10 6.9332370 -27.187482 41.05396 1.0000000
## Ctrl:d15-10%:d10 NA NA NA NA
## 05%:d15-10%:d10 2.9433116 -31.177407 37.06403 1.0000000
## 10%:d15-10%:d10 3.8067391 -30.313980 37.92746 1.0000000
## 25%:d15-10%:d10 -10.9551657 -45.075885 23.16555 0.9999399
## 50%:d15-10%:d10 1.0735171 -33.047202 35.19424 1.0000000
## 50%:d10-25%:d10 4.3452720 -29.775447 38.46599 1.0000000
## Ctrl:d15-25%:d10 NA NA NA NA
## 05%:d15-25%:d10 0.3553466 -33.765372 34.47607 1.0000000
## 10%:d15-25%:d10 1.2187741 -32.901945 35.33949 1.0000000
## 25%:d15-25%:d10 -13.5431307 -47.663850 20.57759 0.9982754
## 50%:d15-25%:d10 -1.5144479 -35.635167 32.60627 1.0000000
## Ctrl:d15-50%:d10 NA NA NA NA
## 05%:d15-50%:d10 -3.9899254 -38.110644 30.13079 1.0000000
## 10%:d15-50%:d10 -3.1264979 -37.247217 30.99422 1.0000000
## 25%:d15-50%:d10 -17.8884027 -52.009122 16.23232 0.9486146
## 50%:d15-50%:d10 -5.8597198 -39.980439 28.26100 1.0000000
## 05%:d15-Ctrl:d15 NA NA NA NA
## 10%:d15-Ctrl:d15 NA NA NA NA
## 25%:d15-Ctrl:d15 NA NA NA NA
## 50%:d15-Ctrl:d15 NA NA NA NA
## 10%:d15-05%:d15 0.8634275 -33.257291 34.98415 1.0000000
## 25%:d15-05%:d15 -13.8984773 -48.019196 20.22224 0.9975085
## 50%:d15-05%:d15 -1.8697944 -35.990513 32.25092 1.0000000
## 25%:d15-10%:d15 -14.7619048 -48.882624 19.35881 0.9943603
## 50%:d15-10%:d15 -2.7332219 -36.853941 31.38750 1.0000000
## 50%:d15-25%:d15 12.0286828 -22.092036 46.14940 0.9997164plot(aov_TT_G)
# Larval_stage x Time
aov_LST_G <- aov(GR_M ~ Larval_stage * Bti_application, data = data[Day == selectday & Ind > 0])
summary(aov_LST_G)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 615 614.8 2.171 0.144
## Bti_application 4 814 203.5 0.718 0.582
## Larval_stage:Bti_application 4 218 54.5 0.192 0.942
## Residuals 89 25210 283.3plot(aov_LST_G)
# Larval_stage x Treatment
aov_LSTr_G <- aov(GR_M ~ Larval_stage * Treatment, data = data[Day == selectday & Ind > 0])
summary(aov_LSTr_G)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 615 614.8 2.401 0.125
## Treatment 4 2057 514.4 2.008 0.100
## Larval_stage:Treatment 4 1392 348.0 1.359 0.255
## Residuals 89 22793 256.1plot(aov_LSTr_G)
# Larval_stage x Treatment x Bti_application
aov_LSTrT_G <- aov(GR_M ~ Larval_stage * Treatment * Bti_application, data = data[Day == selectday & Ind > 0])
summary(aov_LSTrT_G)## Df Sum Sq Mean Sq F value Pr(>F)
## Larval_stage 1 615 614.8 2.244 0.139
## Treatment 4 2057 514.4 1.877 0.125
## Bti_application 3 710 236.8 0.864 0.464
## Larval_stage:Treatment 4 1194 298.4 1.089 0.369
## Larval_stage:Bti_application 3 330 110.1 0.402 0.752
## Treatment:Bti_application 9 3231 359.0 1.310 0.249
## Larval_stage:Treatment:Bti_application 8 637 79.6 0.291 0.967
## Residuals 66 18083 274.0plot(aov_LSTrT_G)
4 Relationship btw. emerged individuals and duration of emergence
The pattern of duration of emergence grouped by application days is similar to the pattern of number of individuals grouped by application days (see RPC report for data visualizations). Therefore, it was further investigated if the duration of emergence is predicted by the number of individuals and if this a stochastic or biological effect.
4.1 Linear regression
lm <- lm(Emergence_all ~ Ind, data[Day==17 & Ind > 0])
summary(lm)##
## Call:
## lm(formula = Emergence_all ~ Ind, data = data[Day == 17 & Ind >
## 0])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1224 -1.0759 -0.1224 0.8776 5.9188
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.85413 0.59549 1.434 0.155
## Ind 0.39176 0.03461 11.320 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.81 on 97 degrees of freedom
## Multiple R-squared: 0.5692, Adjusted R-squared: 0.5647
## F-statistic: 128.1 on 1 and 97 DF, p-value: < 2.2e-16ggscatter(
data[Day == 17 & Ind > 0], x = "Ind", y = "Emergence_all", add = "reg.line") +
geom_point(aes(colour = Bti_application), size = 3) +
scale_colour_manual(values = application) +
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
)## `geom_smooth()` using formula 'y ~ x'
4.2 ANCOVA
- …to control for Bti_application
- Check Assumptions:
- linear relationship between covariate and response variable
- response variable = Emergence duration (Emergence_all)
- covariate = Number of Individuals (Ind)
ggscatter(
data[Day == 17 & Ind > 0], x = "Ind", y = "Emergence_all",
color = "Bti_application", add = "reg.line", size = 3,
)+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Bti_application)
)## `geom_smooth()` using formula 'y ~ x'
lm <- lm(Emergence_all ~ Ind, data[Day==17])
summary(lm)##
## Call:
## lm(formula = Emergence_all ~ Ind, data = data[Day == 17])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0984 -0.9051 -0.0984 0.8449 5.8849
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.64477 0.51089 1.262 0.21
## Ind 0.40335 0.03014 13.383 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.787 on 100 degrees of freedom
## Multiple R-squared: 0.6417, Adjusted R-squared: 0.6381
## F-statistic: 179.1 on 1 and 100 DF, p-value: < 2.2e-16- Homogeneity of regression slopes
data[Day == 17 & Ind > 0] %>% anova_test(Emergence_all ~ Bti_application * Ind)## Coefficient covariances computed by hccm()## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 Bti_application 4 89 0.113 9.77e-01 0.005
## 2 Ind 1 89 91.256 2.69e-15 * 0.506
## 3 Bti_application:Ind 4 89 0.499 7.36e-01 0.022data[Day == 17 & Ind > 0 & Bti_application == "none" | Day == 17 & Ind > 0 & Bti_application != "d0"] %>% anova_test(Emergence_all ~ Bti_application * Ind)## Coefficient covariances computed by hccm()## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 Bti_application 3 70 0.039 9.90e-01 0.002
## 2 Ind 1 70 51.689 5.63e-10 * 0.425
## 3 Bti_application:Ind 3 70 0.535 6.60e-01 0.022The slopes are not parallel. However, the interaction term is not significant, which means that the regression slopes are homogeneous.
Fit the model:
# the covariate goes first!!
model.ancova <- lm(Emergence_all ~ Ind + Bti_application, data = data[Day == 17 & Ind > 0])
summary(model.ancova)##
## Call:
## lm(formula = Emergence_all ~ Ind + Bti_application, data = data[Day ==
## 17 & Ind > 0])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1553 -1.0889 -0.1645 0.9650 5.8638
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.46935 1.02355 0.459 0.648
## Ind 0.40184 0.04161 9.657 1.09e-15 ***
## Bti_applicationd0 0.46201 0.88099 0.524 0.601
## Bti_applicationd05 0.14213 0.84173 0.169 0.866
## Bti_applicationd10 0.13165 0.84253 0.156 0.876
## Bti_applicationd15 0.22828 0.84941 0.269 0.789
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.844 on 93 degrees of freedom
## Multiple R-squared: 0.5713, Adjusted R-squared: 0.5482
## F-statistic: 24.79 on 5 and 93 DF, p-value: 8.627e-16plot(model.ancova) # quickly check assumptions
model.metrics <- augment(model.ancova)Check assumptions of the model: 3) Normality of residuals
hist(model.metrics$.resid)
shapiro_test(model.metrics$.resid)## # A tibble: 1 x 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 model.metrics$.resid 0.948 0.000697Normality of residuals is ok. Shapiro test is significant. ANCOVA is relatively robust against violation of normality.
- homogeneous variance
model.metrics %>% levene_test(.resid ~ Bti_application)## # A tibble: 1 x 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 4 94 0.631 0.641library(car)## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
##
## recodeleveneTest(Emergence_all ~ Bti_application, data = data[Day == 17 & Ind > 0])## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 4 0.9618 0.4323
## 94Variance is homogeneous.
- No outliers
model.metrics %>%
filter(abs(.std.resid) > 3) %>%
as.data.frame()## Emergence_all Ind Bti_application .fitted .resid .hat .sigma
## 1 12 14 d05 6.237222 5.762778 0.04464037 1.749128
## 2 15 21 d15 9.136245 5.863755 0.04294313 1.745505
## .cooksd .std.resid
## 1 0.07961325 3.197333
## 2 0.07901280 3.250471There are two outliers in the data set (only slightly above 3 (std. err.)). We take them as they are because we know about them and decided to leave them in the data set. They are inside cooks distance (see above), so this should be ok (no influential point).
Computation:
res.aov <- data[Day == 17 & Ind > 0] %>% anova_test(Emergence_all ~ Ind + Bti_application)## Coefficient covariances computed by hccm()get_anova_table(res.aov)## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 Ind 1 93 93.265 1.09e-15 * 0.501
## 2 Bti_application 4 93 0.116 9.77e-01 0.005summary(aov(Emergence_all ~ Ind + Bti_application, data = data[Day == 17 & Ind > 0])) #another way to double check## Df Sum Sq Mean Sq F value Pr(>F)
## Ind 1 419.8 419.8 123.467 <2e-16 ***
## Bti_application 4 1.6 0.4 0.116 0.977
## Residuals 93 316.2 3.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1car::Anova(aov(Emergence_all ~ Ind + Bti_application, data = data[Day == 17 & Ind > 0]), type = "III") #another way to double check## Anova Table (Type III tests)
##
## Response: Emergence_all
## Sum Sq Df F value Pr(>F)
## (Intercept) 0.71 1 0.2103 0.6476
## Ind 317.13 1 93.2652 1.085e-15 ***
## Bti_application 1.58 4 0.1160 0.9766
## Residuals 316.23 93
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1After adjustment for the number of individuals, there is no statistically significant difference in the duration of emergence between the different Bti_applications.
Is this correlation between the number of individuals and duration of emergence due to a stochastic or biological effect?
4.3 Permutation
Is this correlation between the number of individuals and duration of emergence due to a stochastic or biological effect? We can test this by resampling the data set by using permuation. In this case, the resampling is a bit tricky. The single sampled individuals need to shuffled and then the duration of emergence and total number of individuals needs to be calculated again for each sample. This is done manually in the next steps because there is no function in R doing this.
#extract information from data set for a better overview
data.perm <- data %>%
select(Name, Rpl, Bti_application, Ind_Single, Day, Larval_stage)
data.perm.L1 <- data.perm[Larval_stage == "L1" & Day > 2] # general emergence start for L1 was later than for Lx
data.perm.Lx <- data.perm[Larval_stage == "Lx" & Day < 16] # general emergence end for Lx was later than for Lx
data.perm.L1 <- data.perm.L1 %>% arrange(Name) # order by Name
data.perm.Lx <- data.perm.Lx %>% arrange(Name) # order by Name
set.seed(1979) # for reproducability of results
n1 <- length(data.perm.L1$Name) # the number of observations to sample
nx <- length(data.perm.Lx$Name) # the number of observations to sample
P <- 100 # the number of permutation samples to take
Ind_L1 <- data.perm.L1$Ind_Single # the variable we will resample from (single sampled (not cumulative) individuals!)
Ind_Lx <- data.perm.Lx$Ind_Single # the variable we will resample from (single sampled (not cumulative) individuals!)
PermSamplesL1 <- matrix(0, nrow=n1, ncol=P) # initialize a matrix to store the permutation data
PermSamplesLx <- matrix(0, nrow=nx, ncol=P) # initialize a matrix to store the permutation data
# each column is a permutation sample of data
# get those permutation samples
for(i in 1:P){
PermSamplesL1[,i] <- sample(Ind_L1, size= n1, replace=FALSE)
}
for(i in 1:P){
PermSamplesLx[,i] <- sample(Ind_Lx, size= nx, replace=FALSE)
}
PermSamplesL1 <- as.data.table(PermSamplesL1)
PermSamplesLx <- as.data.table(PermSamplesLx)
# combine data
perm.L1v1 <- cbind(data.perm.L1, PermSamplesL1$V1)
perm.Lxv1 <- cbind(data.perm.Lx, PermSamplesLx$V1)
perm.L1v2 <- cbind(data.perm.L1, PermSamplesL1$V2)
perm.Lxv2 <- cbind(data.perm.Lx, PermSamplesLx$V2)
perm.L1v3 <- cbind(data.perm.L1, PermSamplesL1$V3)
perm.Lxv3 <- cbind(data.perm.Lx, PermSamplesLx$V3)
perm.L1v4 <- cbind(data.perm.L1, PermSamplesL1$V4)
perm.Lxv4 <- cbind(data.perm.Lx, PermSamplesLx$V4)
perm.L1v5 <- cbind(data.perm.L1, PermSamplesL1$V5)
perm.Lxv5 <- cbind(data.perm.Lx, PermSamplesLx$V5)
perm.L1v6 <- cbind(data.perm.L1, PermSamplesL1$V6)
perm.Lxv6 <- cbind(data.perm.Lx, PermSamplesLx$V6)
perm.L1v7 <- cbind(data.perm.L1, PermSamplesL1$V7)
perm.Lxv7 <- cbind(data.perm.Lx, PermSamplesLx$V7)
perm.L1v8 <- cbind(data.perm.L1, PermSamplesL1$V8)
perm.Lxv8 <- cbind(data.perm.Lx, PermSamplesLx$V8)
perm.L1v9 <- cbind(data.perm.L1, PermSamplesL1$V9)
perm.Lxv9 <- cbind(data.perm.Lx, PermSamplesLx$V9)
perm.L1v10 <- cbind(data.perm.L1, PermSamplesL1$V10)
perm.Lxv10 <- cbind(data.perm.Lx, PermSamplesLx$V10)
perm.L1v11 <- cbind(data.perm.L1, PermSamplesL1$V11)
perm.Lxv11 <- cbind(data.perm.Lx, PermSamplesLx$V11)
perm.L1v12 <- cbind(data.perm.L1, PermSamplesL1$V12)
perm.Lxv12 <- cbind(data.perm.Lx, PermSamplesLx$V12)
perm.L1v13 <- cbind(data.perm.L1, PermSamplesL1$V13)
perm.Lxv13 <- cbind(data.perm.Lx, PermSamplesLx$V13)
perm.L1v14 <- cbind(data.perm.L1, PermSamplesL1$V14)
perm.Lxv14 <- cbind(data.perm.Lx, PermSamplesLx$V14)
perm.L1v15 <- cbind(data.perm.L1, PermSamplesL1$V15)
perm.Lxv15 <- cbind(data.perm.Lx, PermSamplesLx$V15)
perm.L1v16 <- cbind(data.perm.L1, PermSamplesL1$V16)
perm.Lxv16 <- cbind(data.perm.Lx, PermSamplesLx$V16)
perm.L1v17 <- cbind(data.perm.L1, PermSamplesL1$V17)
perm.Lxv17 <- cbind(data.perm.Lx, PermSamplesLx$V17)
perm.L1v18 <- cbind(data.perm.L1, PermSamplesL1$V18)
perm.Lxv18 <- cbind(data.perm.Lx, PermSamplesLx$V18)
perm.L1v19 <- cbind(data.perm.L1, PermSamplesL1$V19)
perm.Lxv19 <- cbind(data.perm.Lx, PermSamplesLx$V19)
perm.L1v20 <- cbind(data.perm.L1, PermSamplesL1$V20)
perm.Lxv20 <- cbind(data.perm.Lx, PermSamplesLx$V20)
perm.L1v21 <- cbind(data.perm.L1, PermSamplesL1$V21)
perm.Lxv21 <- cbind(data.perm.Lx, PermSamplesLx$V21)
perm.L1v22 <- cbind(data.perm.L1, PermSamplesL1$V22)
perm.Lxv22 <- cbind(data.perm.Lx, PermSamplesLx$V22)
perm.L1v23 <- cbind(data.perm.L1, PermSamplesL1$V23)
perm.Lxv23 <- cbind(data.perm.Lx, PermSamplesLx$V23)
perm.L1v24 <- cbind(data.perm.L1, PermSamplesL1$V24)
perm.Lxv24 <- cbind(data.perm.Lx, PermSamplesLx$V24)
perm.L1v25 <- cbind(data.perm.L1, PermSamplesL1$V25)
perm.Lxv25 <- cbind(data.perm.Lx, PermSamplesLx$V25)
perm.L1v26 <- cbind(data.perm.L1, PermSamplesL1$V26)
perm.Lxv26 <- cbind(data.perm.Lx, PermSamplesLx$V26)
perm.L1v27 <- cbind(data.perm.L1, PermSamplesL1$V27)
perm.Lxv27 <- cbind(data.perm.Lx, PermSamplesLx$V27)
perm.L1v28 <- cbind(data.perm.L1, PermSamplesL1$V28)
perm.Lxv28 <- cbind(data.perm.Lx, PermSamplesLx$V28)
perm.L1v29 <- cbind(data.perm.L1, PermSamplesL1$V29)
perm.Lxv29 <- cbind(data.perm.Lx, PermSamplesLx$V29)
perm.L1v30 <- cbind(data.perm.L1, PermSamplesL1$V30)
perm.Lxv30 <- cbind(data.perm.Lx, PermSamplesLx$V30)
perm.L1v31 <- cbind(data.perm.L1, PermSamplesL1$V31)
perm.Lxv31 <- cbind(data.perm.Lx, PermSamplesLx$V31)
perm.L1v32 <- cbind(data.perm.L1, PermSamplesL1$V32)
perm.Lxv32 <- cbind(data.perm.Lx, PermSamplesLx$V32)
perm.L1v33 <- cbind(data.perm.L1, PermSamplesL1$V33)
perm.Lxv33 <- cbind(data.perm.Lx, PermSamplesLx$V33)
perm.L1v34 <- cbind(data.perm.L1, PermSamplesL1$V34)
perm.Lxv34 <- cbind(data.perm.Lx, PermSamplesLx$V34)
perm.L1v35 <- cbind(data.perm.L1, PermSamplesL1$V35)
perm.Lxv35 <- cbind(data.perm.Lx, PermSamplesLx$V15)
perm.L1v36 <- cbind(data.perm.L1, PermSamplesL1$V36)
perm.Lxv36 <- cbind(data.perm.Lx, PermSamplesLx$V16)
perm.L1v37 <- cbind(data.perm.L1, PermSamplesL1$V37)
perm.Lxv37 <- cbind(data.perm.Lx, PermSamplesLx$V37)
perm.L1v38 <- cbind(data.perm.L1, PermSamplesL1$V38)
perm.Lxv38 <- cbind(data.perm.Lx, PermSamplesLx$V38)
perm.L1v39 <- cbind(data.perm.L1, PermSamplesL1$V39)
perm.Lxv39 <- cbind(data.perm.Lx, PermSamplesLx$V39)
perm.L1v40 <- cbind(data.perm.L1, PermSamplesL1$V40)
perm.Lxv40 <- cbind(data.perm.Lx, PermSamplesLx$V40)
perm.L1v41 <- cbind(data.perm.L1, PermSamplesL1$V41)
perm.Lxv41 <- cbind(data.perm.Lx, PermSamplesLx$V41)
perm.L1v42 <- cbind(data.perm.L1, PermSamplesL1$V42)
perm.Lxv42 <- cbind(data.perm.Lx, PermSamplesLx$V42)
perm.L1v43 <- cbind(data.perm.L1, PermSamplesL1$V43)
perm.Lxv43 <- cbind(data.perm.Lx, PermSamplesLx$V43)
perm.L1v44 <- cbind(data.perm.L1, PermSamplesL1$V44)
perm.Lxv44 <- cbind(data.perm.Lx, PermSamplesLx$V44)
perm.L1v45 <- cbind(data.perm.L1, PermSamplesL1$V45)
perm.Lxv45 <- cbind(data.perm.Lx, PermSamplesLx$V45)
perm.L1v46 <- cbind(data.perm.L1, PermSamplesL1$V46)
perm.Lxv46 <- cbind(data.perm.Lx, PermSamplesLx$V46)
perm.L1v47 <- cbind(data.perm.L1, PermSamplesL1$V47)
perm.Lxv47 <- cbind(data.perm.Lx, PermSamplesLx$V47)
perm.L1v48 <- cbind(data.perm.L1, PermSamplesL1$V48)
perm.Lxv48 <- cbind(data.perm.Lx, PermSamplesLx$V48)
perm.L1v49 <- cbind(data.perm.L1, PermSamplesL1$V49)
perm.Lxv49 <- cbind(data.perm.Lx, PermSamplesLx$V49)
perm.L1v50 <- cbind(data.perm.L1, PermSamplesL1$V50)
perm.Lxv50 <- cbind(data.perm.Lx, PermSamplesLx$V50)
perm.L1v51 <- cbind(data.perm.L1, PermSamplesL1$V51)
perm.Lxv51 <- cbind(data.perm.Lx, PermSamplesLx$V51)
perm.L1v52 <- cbind(data.perm.L1, PermSamplesL1$V52)
perm.Lxv52 <- cbind(data.perm.Lx, PermSamplesLx$V52)
perm.L1v53 <- cbind(data.perm.L1, PermSamplesL1$V53)
perm.Lxv53 <- cbind(data.perm.Lx, PermSamplesLx$V53)
perm.L1v54 <- cbind(data.perm.L1, PermSamplesL1$V54)
perm.Lxv54 <- cbind(data.perm.Lx, PermSamplesLx$V54)
perm.L1v55 <- cbind(data.perm.L1, PermSamplesL1$V55)
perm.Lxv55 <- cbind(data.perm.Lx, PermSamplesLx$V55)
perm.L1v56 <- cbind(data.perm.L1, PermSamplesL1$V56)
perm.Lxv56 <- cbind(data.perm.Lx, PermSamplesLx$V56)
perm.L1v57 <- cbind(data.perm.L1, PermSamplesL1$V57)
perm.Lxv57 <- cbind(data.perm.Lx, PermSamplesLx$V57)
perm.L1v58 <- cbind(data.perm.L1, PermSamplesL1$V58)
perm.Lxv58 <- cbind(data.perm.Lx, PermSamplesLx$V58)
perm.L1v59 <- cbind(data.perm.L1, PermSamplesL1$V59)
perm.Lxv59 <- cbind(data.perm.Lx, PermSamplesLx$V59)
perm.L1v60 <- cbind(data.perm.L1, PermSamplesL1$V60)
perm.Lxv60 <- cbind(data.perm.Lx, PermSamplesLx$V60)
perm.L1v61 <- cbind(data.perm.L1, PermSamplesL1$V61)
perm.Lxv61 <- cbind(data.perm.Lx, PermSamplesLx$V61)
perm.L1v62 <- cbind(data.perm.L1, PermSamplesL1$V62)
perm.Lxv62 <- cbind(data.perm.Lx, PermSamplesLx$V62)
perm.L1v63 <- cbind(data.perm.L1, PermSamplesL1$V63)
perm.Lxv63 <- cbind(data.perm.Lx, PermSamplesLx$V63)
perm.L1v64 <- cbind(data.perm.L1, PermSamplesL1$V64)
perm.Lxv64 <- cbind(data.perm.Lx, PermSamplesLx$V64)
perm.L1v65 <- cbind(data.perm.L1, PermSamplesL1$V65)
perm.Lxv65 <- cbind(data.perm.Lx, PermSamplesLx$V65)
perm.L1v66 <- cbind(data.perm.L1, PermSamplesL1$V66)
perm.Lxv66 <- cbind(data.perm.Lx, PermSamplesLx$V66)
perm.L1v67 <- cbind(data.perm.L1, PermSamplesL1$V67)
perm.Lxv67 <- cbind(data.perm.Lx, PermSamplesLx$V67)
perm.L1v68 <- cbind(data.perm.L1, PermSamplesL1$V68)
perm.Lxv68 <- cbind(data.perm.Lx, PermSamplesLx$V68)
perm.L1v69 <- cbind(data.perm.L1, PermSamplesL1$V69)
perm.Lxv69 <- cbind(data.perm.Lx, PermSamplesLx$V69)
perm.L1v70 <- cbind(data.perm.L1, PermSamplesL1$V70)
perm.Lxv70 <- cbind(data.perm.Lx, PermSamplesLx$V70)
perm.L1v71 <- cbind(data.perm.L1, PermSamplesL1$V71)
perm.Lxv71 <- cbind(data.perm.Lx, PermSamplesLx$V71)
perm.L1v72 <- cbind(data.perm.L1, PermSamplesL1$V72)
perm.Lxv72 <- cbind(data.perm.Lx, PermSamplesLx$V72)
perm.L1v73 <- cbind(data.perm.L1, PermSamplesL1$V73)
perm.Lxv73 <- cbind(data.perm.Lx, PermSamplesLx$V73)
perm.L1v74 <- cbind(data.perm.L1, PermSamplesL1$V74)
perm.Lxv74 <- cbind(data.perm.Lx, PermSamplesLx$V74)
perm.L1v75 <- cbind(data.perm.L1, PermSamplesL1$V75)
perm.Lxv75 <- cbind(data.perm.Lx, PermSamplesLx$V75)
perm.L1v76 <- cbind(data.perm.L1, PermSamplesL1$V76)
perm.Lxv76 <- cbind(data.perm.Lx, PermSamplesLx$V76)
perm.L1v77 <- cbind(data.perm.L1, PermSamplesL1$V77)
perm.Lxv77 <- cbind(data.perm.Lx, PermSamplesLx$V77)
perm.L1v78 <- cbind(data.perm.L1, PermSamplesL1$V78)
perm.Lxv78 <- cbind(data.perm.Lx, PermSamplesLx$V78)
perm.L1v79 <- cbind(data.perm.L1, PermSamplesL1$V79)
perm.Lxv79 <- cbind(data.perm.Lx, PermSamplesLx$V79)
perm.L1v80 <- cbind(data.perm.L1, PermSamplesL1$V80)
perm.Lxv80 <- cbind(data.perm.Lx, PermSamplesLx$V80)
perm.L1v81 <- cbind(data.perm.L1, PermSamplesL1$V81)
perm.Lxv81 <- cbind(data.perm.Lx, PermSamplesLx$V81)
perm.L1v82 <- cbind(data.perm.L1, PermSamplesL1$V82)
perm.Lxv82 <- cbind(data.perm.Lx, PermSamplesLx$V82)
perm.L1v83 <- cbind(data.perm.L1, PermSamplesL1$V83)
perm.Lxv83 <- cbind(data.perm.Lx, PermSamplesLx$V83)
perm.L1v84 <- cbind(data.perm.L1, PermSamplesL1$V84)
perm.Lxv84 <- cbind(data.perm.Lx, PermSamplesLx$V84)
perm.L1v85 <- cbind(data.perm.L1, PermSamplesL1$V85)
perm.Lxv85 <- cbind(data.perm.Lx, PermSamplesLx$V85)
perm.L1v86 <- cbind(data.perm.L1, PermSamplesL1$V86)
perm.Lxv86 <- cbind(data.perm.Lx, PermSamplesLx$V86)
perm.L1v87 <- cbind(data.perm.L1, PermSamplesL1$V87)
perm.Lxv87 <- cbind(data.perm.Lx, PermSamplesLx$V87)
perm.L1v88 <- cbind(data.perm.L1, PermSamplesL1$V88)
perm.Lxv88 <- cbind(data.perm.Lx, PermSamplesLx$V88)
perm.L1v89 <- cbind(data.perm.L1, PermSamplesL1$V89)
perm.Lxv89 <- cbind(data.perm.Lx, PermSamplesLx$V89)
perm.L1v90 <- cbind(data.perm.L1, PermSamplesL1$V90)
perm.Lxv90 <- cbind(data.perm.Lx, PermSamplesLx$V90)
perm.L1v91 <- cbind(data.perm.L1, PermSamplesL1$V91)
perm.Lxv91 <- cbind(data.perm.Lx, PermSamplesLx$V91)
perm.L1v92 <- cbind(data.perm.L1, PermSamplesL1$V92)
perm.Lxv92 <- cbind(data.perm.Lx, PermSamplesLx$V92)
perm.L1v93 <- cbind(data.perm.L1, PermSamplesL1$V93)
perm.Lxv93 <- cbind(data.perm.Lx, PermSamplesLx$V93)
perm.L1v94 <- cbind(data.perm.L1, PermSamplesL1$V94)
perm.Lxv94 <- cbind(data.perm.Lx, PermSamplesLx$V94)
perm.L1v95 <- cbind(data.perm.L1, PermSamplesL1$V95)
perm.Lxv95 <- cbind(data.perm.Lx, PermSamplesLx$V95)
perm.L1v96 <- cbind(data.perm.L1, PermSamplesL1$V96)
perm.Lxv96 <- cbind(data.perm.Lx, PermSamplesLx$V96)
perm.L1v97 <- cbind(data.perm.L1, PermSamplesL1$V97)
perm.Lxv97 <- cbind(data.perm.Lx, PermSamplesLx$V97)
perm.L1v98 <- cbind(data.perm.L1, PermSamplesL1$V98)
perm.Lxv98 <- cbind(data.perm.Lx, PermSamplesLx$V98)
perm.L1v99 <- cbind(data.perm.L1, PermSamplesL1$V99)
perm.Lxv99 <- cbind(data.perm.Lx, PermSamplesLx$V99)
perm.L1v100 <- cbind(data.perm.L1, PermSamplesL1$V100)
perm.Lxv100 <- cbind(data.perm.Lx, PermSamplesLx$V100)
allframes <- list(perm.L1v1, perm.Lxv1,
perm.L1v2, perm.Lxv2,
perm.L1v3, perm.Lxv3,
perm.L1v4, perm.Lxv4,
perm.L1v5, perm.Lxv5,
perm.L1v6, perm.Lxv6,
perm.L1v7, perm.Lxv7,
perm.L1v8, perm.Lxv8,
perm.L1v9, perm.Lxv9,
perm.L1v10, perm.Lxv10,
perm.L1v11, perm.Lxv11,
perm.L1v12, perm.Lxv12,
perm.L1v13, perm.Lxv13,
perm.L1v14, perm.Lxv14,
perm.L1v15, perm.Lxv15,
perm.L1v16, perm.Lxv16,
perm.L1v17, perm.Lxv17,
perm.L1v18, perm.Lxv18,
perm.L1v19, perm.Lxv19,
perm.L1v20, perm.Lxv20,
perm.L1v21, perm.Lxv21,
perm.L1v22, perm.Lxv22,
perm.L1v23, perm.Lxv23,
perm.L1v24, perm.Lxv24,
perm.L1v25, perm.Lxv25,
perm.L1v26, perm.Lxv26,
perm.L1v27, perm.Lxv27,
perm.L1v28, perm.Lxv28,
perm.L1v29, perm.Lxv29,
perm.L1v30, perm.Lxv30,
perm.L1v31, perm.Lxv31,
perm.L1v32, perm.Lxv32,
perm.L1v33, perm.Lxv33,
perm.L1v34, perm.Lxv34,
perm.L1v35, perm.Lxv35,
perm.L1v36, perm.Lxv36,
perm.L1v37, perm.Lxv37,
perm.L1v38, perm.Lxv38,
perm.L1v39, perm.Lxv39,
perm.L1v40, perm.Lxv40,
perm.L1v41, perm.Lxv41,
perm.L1v42, perm.Lxv42,
perm.L1v43, perm.Lxv43,
perm.L1v44, perm.Lxv44,
perm.L1v45, perm.Lxv45,
perm.L1v46, perm.Lxv46,
perm.L1v47, perm.Lxv47,
perm.L1v48, perm.Lxv48,
perm.L1v49, perm.Lxv49,
perm.L1v50, perm.Lxv50,
perm.L1v51, perm.Lxv51,
perm.L1v52, perm.Lxv52,
perm.L1v53, perm.Lxv53,
perm.L1v54, perm.Lxv54,
perm.L1v55, perm.Lxv55,
perm.L1v56, perm.Lxv56,
perm.L1v57, perm.Lxv57,
perm.L1v58, perm.Lxv58,
perm.L1v59, perm.Lxv59,
perm.L1v60, perm.Lxv60,
perm.L1v61, perm.Lxv61,
perm.L1v62, perm.Lxv62,
perm.L1v63, perm.Lxv63,
perm.L1v64, perm.Lxv64,
perm.L1v65, perm.Lxv65,
perm.L1v66, perm.Lxv66,
perm.L1v67, perm.Lxv67,
perm.L1v68, perm.Lxv68,
perm.L1v69, perm.Lxv69,
perm.L1v70, perm.Lxv70,
perm.L1v71, perm.Lxv71,
perm.L1v72, perm.Lxv72,
perm.L1v73, perm.Lxv73,
perm.L1v74, perm.Lxv74,
perm.L1v75, perm.Lxv75,
perm.L1v76, perm.Lxv76,
perm.L1v77, perm.Lxv77,
perm.L1v78, perm.Lxv78,
perm.L1v79, perm.Lxv79,
perm.L1v80, perm.Lxv80,
perm.L1v81, perm.Lxv81,
perm.L1v82, perm.Lxv82,
perm.L1v83, perm.Lxv83,
perm.L1v84, perm.Lxv84,
perm.L1v85, perm.Lxv85,
perm.L1v86, perm.Lxv86,
perm.L1v87, perm.Lxv87,
perm.L1v88, perm.Lxv88,
perm.L1v89, perm.Lxv89,
perm.L1v90, perm.Lxv90,
perm.L1v91, perm.Lxv91,
perm.L1v92, perm.Lxv92,
perm.L1v93, perm.Lxv93,
perm.L1v94, perm.Lxv94,
perm.L1v95, perm.Lxv95,
perm.L1v96, perm.Lxv96,
perm.L1v97, perm.Lxv97,
perm.L1v98, perm.Lxv98,
perm.L1v99, perm.Lxv99,
perm.L1v100, perm.Lxv100)
# edit all frames
framies <- lapply(allframes, function(x) {
x %>%
mutate(Name = as.factor(Name)) %>%
group_by(Name) %>%
mutate(Ind = sum(V2)) %>%
filter(V2 > 0)%>%
mutate(max = max(Day)) %>%
mutate(min = min(Day)) %>%
mutate(Emergence_Ind = max - min) %>%
distinct(Name, .keep_all= TRUE)
} )
onebigframe <- bind_rows(framies, .id = "column_label")
onebigframe <- as.data.table(onebigframe)
onebigframe %>% anova_test(Emergence_Ind ~ column_label * Ind)## Coefficient covariances computed by hccm()## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 column_label 199 9788 4.516 2.47e-84 * 0.084
## 2 Ind 1 9788 2481.018 0.00e+00 * 0.202
## 3 column_label:Ind 199 9788 1.773 2.06e-10 * 0.035onebigframe %>% anova_test(Emergence_Ind ~ column_label * Ind)## Coefficient covariances computed by hccm()## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 column_label 199 9788 4.516 2.47e-84 * 0.084
## 2 Ind 1 9788 2481.018 0.00e+00 * 0.202
## 3 column_label:Ind 199 9788 1.773 2.06e-10 * 0.035# linear models per data frame
fits <- lapply(framies, function(x) {
lm(formula = Emergence_Ind ~ Ind, data = x )
} )
# extract information from all models
r.squared <- sapply(fits, function(x) summary(x)$r.squared )
p.value <- sapply(fits, function(x) summary(x)$coefficients[2,4] )
slope <- sapply(fits, function(x) summary(x)$coefficients[2,1] )
# compare with original
lm <- lm(Emergence_all ~ Ind, data[Day==17 & Ind > 0])
summary(lm)##
## Call:
## lm(formula = Emergence_all ~ Ind, data = data[Day == 17 & Ind >
## 0])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1224 -1.0759 -0.1224 0.8776 5.9188
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.85413 0.59549 1.434 0.155
## Ind 0.39176 0.03461 11.320 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.81 on 97 degrees of freedom
## Multiple R-squared: 0.5692, Adjusted R-squared: 0.5647
## F-statistic: 128.1 on 1 and 97 DF, p-value: < 2.2e-16# make up original data
datalm <- data[Day == 17] %>%
select(Name, Rpl, Bti_application, Ind, Day, Larval_stage, Emergence_all) %>%
rename(Emergence_Ind = Emergence_all)
datalm$group <- "A"
# make up permuted data
onebigframelm <- onebigframe %>%
select(Name, Rpl, Bti_application, Ind, Day, Larval_stage, Emergence_Ind)
onebigframelm$group <- "B"
# merge original and permuted data
allframeslm <- rbind(datalm, onebigframelm)4.4 Compare permuted/original data
# original
modelA <- lm(Emergence_Ind ~ Ind, allframeslm[group == 'A' & Ind > 0])
summary(modelA)##
## Call:
## lm(formula = Emergence_Ind ~ Ind, data = allframeslm[group ==
## "A" & Ind > 0])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1224 -1.0759 -0.1224 0.8776 5.9188
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.85413 0.59549 1.434 0.155
## Ind 0.39176 0.03461 11.320 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.81 on 97 degrees of freedom
## Multiple R-squared: 0.5692, Adjusted R-squared: 0.5647
## F-statistic: 128.1 on 1 and 97 DF, p-value: < 2.2e-16# permuted
modelB <- lm(Emergence_Ind ~ Ind, allframeslm[group == 'B' & Ind > 0])
summary(modelB)##
## Call:
## lm(formula = Emergence_Ind ~ Ind, data = allframeslm[group ==
## "B" & Ind > 0])
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.4189 -1.3618 0.4268 1.8497 6.0781
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.499083 0.067862 110.50 <2e-16 ***
## Ind 0.211421 0.003951 53.51 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.581 on 10186 degrees of freedom
## Multiple R-squared: 0.2194, Adjusted R-squared: 0.2194
## F-statistic: 2864 on 1 and 10186 DF, p-value: < 2.2e-16# are the slopes significantly different?
anova <- aov(Emergence_Ind ~ group * Ind, allframeslm[Ind > 0])
summary(anova) # significant## Df Sum Sq Mean Sq F value Pr(>F)
## group 1 1264 1264 190.63 < 2e-16 ***
## Ind 1 19408 19408 2927.49 < 2e-16 ***
## group:Ind 1 88 88 13.33 0.000262 ***
## Residuals 10283 68173 7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1library(sjstats)## Registered S3 methods overwritten by 'lme4':
## method from
## cooks.distance.influence.merMod car
## influence.merMod car
## dfbeta.influence.merMod car
## dfbetas.influence.merMod car##
## Attaching package: 'sjstats'## The following object is masked from 'package:broom':
##
## bootstrapeffectsize::eta_squared(anova, partial = FALSE) # always report effect sizes## Parameter | Eta2 | 90% CI
## -----------------------------------
## group | 0.01 | [0.01, 0.02]
## Ind | 0.22 | [0.21, 0.23]
## group:Ind | 9.94e-04 | [0.00, 0.00]ggscatter(
allframeslm[Ind > 0], x = "Ind", y = "Emergence_Ind",
color = "group", add = "reg.line", size = "group")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = group),
label.x = c(25,25), label.y = c(3.5,5), size = 3.5) +
scale_size_manual(values=c(3,1), guide = "none")+
scale_color_manual(values = c("red3", "royalblue2"), labels = c("A" = "Original data",
"B" = "Permuted data"))+
ylab("Emergence durationt [days]") +
xlab("Number of emerged individuals") +
theme(legend.position = "bottom",
legend.title = element_blank(), legend.text = element_text(size = 13),
legend.key = element_blank(),
plot.title = element_text(size = 13, face = "bold"),
panel.background = element_rect(fill = "white", colour = "black", size = 0.5, linetype = "solid"),
panel.border = element_rect(colour = "black", fill = NA),
strip.text.x = element_text(size = 11, face = "bold"), strip.background = element_blank(),
axis.title = element_text(size = 15),
axis.title.x = element_text(margin = margin(10, 10, -10, 10)),
axis.title.y = element_text(margin = margin(10, 10, -10, 10)),
axis.text = element_text(size = 13),
plot.margin = margin(1, 1, 1, 1)
) ## `geom_smooth()` using formula 'y ~ x'